Become a skilled data analyst. Join now to learn from a competent industry practitioner.
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Corporate Served- Recently, there has been a monstrous rush in the reception of data analytics to change and improve business cycles and that pattern isn't disappearing. The utilization of data is at its peak, not an inquiry yet an unquestionable requirement as it allows companies to settle on better-informed decisions because of the strong data analysis tools and libraries now accessible.
- In fact, Data analyst jobs can be found via multiple mixes of companies and industries. Any company that utilizes data requires data analysts to examine and make sense of it. Therefore, jobs in the data analytics sector are enormous, salaries are high, and the career paths you can take are abundant. So, if you genuinely have an interest in building a career out of this course, you must not delay, and get associated with a reputed Data Analyst Training Institute in Noida to examine each stage closely.
- To obtain its in-depth information, you must get related with a decent provider of Data Analyst Course in Noida that will help you not only analyse each function theoretically but practically as well. In the market as well, plenty of educational set-ups will help you know the whole functioning of data analytics in a much better way. So, kick-start your career, and the best use of your technical abilities.
Data Analyst Training in Noida
- By enrolling in our Data Analyst Training in Noida, you will get the chance to acquire some valuable information from numerous examples, study material, etc. Let us proceed further and know what you will cover in this course.
- Right at the beginning of Data Analyst Course in Noida:
You will get to know about its fundamentals & basics.
How to examine data and how to utilize statistics in practice, explain different behaviours and events, analyse, and create data for the analysis, and how to accumulate data respectively.
Data planning, data exploration, data collection, data analysis, etc.
Grasp details about the latest trends, information regarding handling data, and the whole procedure.
- Skilled Data Analysts are paid hugely well. So, if you are concerned about its salary slab, then do not bother, and get yourself enrolled in Data Analyst Training Course in Noida to get started with this field immediately.
- To know the salary figures, refer to the points mentioned below.
A fresher Data Analyst earns Rs. 3,56,363 p.a.
Likewise, on the other hand, an experienced and knowledgeable Data Analyst earns Rs. 11.6 Lakhs yearly.
By gaining more work experience, and skills, your salary package will get uplifted positively.
You will also end up making more money by turning into a freelancer also.
- Data Analyst is one of the most sought-after jobs in the world. This is so, because they are probably always in demand and receives a higher salary package. Data Analysis has a bright future ahead as well, and that’s why candidates also seem to be interested in the Data Analyst Training in Noida lately.
By having a legit accreditation for the Data Analyst Course in Noida in hand, you will turn into a certified, and skilled Data Analyst and might get the opportunity to get into well-established companies.
By enrolling yourself in the Data Analyst Training Course in Noida, you will get to know about its related job roles as well.
You will also earn a bit higher salary structure than other employees.
- There are various highlighting series of reasons why you should get started with Data Analyst Training in Noida, one of the main reasons is its huge demand, and vibrant future.
- The mentioned points clearly discuss the topmost reasons get started with a legit Data Analyst Training Institute in Noida respectively.
Data Analysis is in demand almost in every for sort of organizations.
Higher job possibilities.
Increasing salaries for knowledgeable and experienced Data Analysts.
Various work opportunities in a wide series of industries.
Your skills will be utilized to take some effective business decisions.
Numerous opportunities for freelancing.
- A Data Analyst is accountable to execute numerous data handling tasks daily. Here, at Croma Campus, we will also help you know important skills, and necessary information related to the subject. To know the job roles in detail, you must get started with the Data Analyst Course in Noida to examine each role in detail.
- Your job role will might differ as per unlike projects but the main duties will eventually be constant. So, make the most of Data Analyst Training Course in Noida, and prepare yourself well to soar high in this direction.
Working as Data Analyst will also indulge you in gathering data, conducting surveys, keeping a track of visitors' characteristics, etc.
Likewise, you will also have to clean data, which eventually indicates you will have to keep up with the good quality of data in a spreadsheet to avoid any discrepancies.
You will have to craft and design the basic structures of a database.
You will also be responsible to decide what types of data to store and collect, construct how data categories are interrelated to each other and work via how the data appears.
Elucidating data will also include identifying patterns or trends in data that will help you to answer the question at hand.
You will also have to maintain a decent bond with the shareholders, and your clients to know what they want.
- Currently, you will find numerous top-notch high-level companies looking, for and hiring skilled Data Analysts. So, if you also want to get placed in a well-established company or multi-nationals, knowing its minute details from a legit Data Analyst Training Institute in Noida will be beneficial for your career.
- Refer to the points below to know some of the well-established companies hiring skilled analysts.
Pleasant Inc, DSI Services, Stamford India Pvt. Ltd., etc. are some of the well-known companies hiring Data Analysts.
By getting in touch with a decent provider of Data Analyst Training in Noida, you will surely end up getting into these big-sized companies.
Our qualified trainers will also help you in clearing the interview by often organizing a mock test.
The main motive of the Data Analyst Course in Noida is to help you to get placed in a well-established organization.
- For the past few years, Croma Campus has been referred to as the best provider of Data Analyst Training Course in Noida. This is so, because, we target at delivering qualitative training along with numerous instances, study material, and adequate guidance by experienced faculty members.
- Well, right from the initial level, our trainers will give you suggestive tips to clear the interview process and so that you can acquire the benefits from Data Analyst Training Institute in Noida progressively.
So, if you are also looking to obtain detailed information regarding Data Analysis, getting associated with a decent educational Data Analyst Training in Noida will be an ideal move toward your career.
Here, along with a legit certification in hand, you will also get enough chances to brush up on your existing skills, and imbibe new ones regarding Data Analyst Course in Noida respectively.
Here, you will obtain information concerning its related course as well.
Croma Campus will offer you placement assistance.
- Related Courses to Data Analyst Training in Noida
Why you should get started with the Data Analyst Course?
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Plenary for Data Analyst Certification Training
| Track | Week Days | Weekends | Fast Track |
|---|---|---|---|
| Course Duration | 40-45 Days | 7 Weekends | 8Days |
| Hours | 1 Hrs. Per Day | 2Hrs. Per Day | 6+ Hrs. Per Day |
| Training Mode | Classroom/Online | Classroom/Online | Classroom/Online |
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Data Analyst Certification Training
- Data Ananyst is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents. It's a 9 months Master’s Program in Data Analyst with Power BI, Tableau & R (including Data Visualization & Cloud Implementation) which includes a 6 months online project internship.
- Data Analyst - Live Projects
- Cloud: Microsoft Azure Fundamentals
- Cloud: AWS(Amazon Web Services)
- Analytics with Tableau
- Analytics with R Proramming
- Analytics Microsoft Power BI
- Analytics with Excel
- Statistics for Data Analyst
- Databases – MS SQL and SQL Queries
- Data Analysis and Visualization
- Python for Data Analyst
Things you will learn:
- Data Ananyst is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents.
- How to get any les and folder’s details using OS
- Handle the File System Organization using OS
- Get the path of Desktop, Documents, Downloads etc.
- How to get user prole detail
- How to split path and extension
- Check Dirs. (exist or not)
- More example of Python Function
- Formatting the Excel File Data
- Creating and Removing Sheets
- Play with Workbook, Sheets and Cells in Excel using Python
- ArithmeticOperation in Excel le.
- Adjusting Rows and Column using Python
- Python | Writing an excel le
- Python| Reading an excel le
- Writing to an excel sheet using Python
- Reading an excel le using Python
- Working With Excel
- Reading from le and sending emails to all users
- Sending Email
- Installing SMTP Python Module
- Programming using database connections
- Reading and Storing cong information on database
- Creating and searching tables
- SQL Database connection using
- Changing Directory
- List Directories and Files
- Making New Directory
- Python Directory and File Management
- Renaming and Deleting Files in Python
- Writing to a File
- Reading from a File
- Closing File
- Python File Modes
- Opening a File
- Try and Finally
- Catching Specic Exception in Python
- Raising Exception
- Catching Exceptions in Python
- Exception handing Try, Except and Finally
- Python Errors and Built-in-Exceptions
- PythonSet Operations
- Remove elements from a Set
- Add element to a Set
- Set Creation
- What is Set
- Frozen Sets
- PythonSet Operations
- Remove elements from a Set
- Add element to a Set
- Set Creation
- What is Set
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
- Looping Dictionaries
- Ordered Dictionaries
- Default Dictionaries
- Dict Comprehension
- Dict Values
- Dict Keys (Updating, Removing and Iterating)
- Dict Items
- Dict From Keys.
- Dict Copy
- Dict Add or Modify Elements
- Dict Get Method
- Dict Access (Accessing Dict Values)
- Dict Creation
- TupleBuilt in Function (Length, Sort)
- Tuple Membership
- Tuple Index
- Tuple Count
- Tuple Deletion
- Changing a Tuple
- Accessing Elements in Tuple
- Tuple Creation
- What is Tuple
- List Comprehension and Nested Comprehension
- List count and Looping
- List Slicing
- List Indexing
- String Split to create a List
- List having Multiple Reference
- List Sorting
- List Revers
- List related Keyword in Python
- List Delete
- List Append & Extend using “+” and Keyword
- List Remove
- List Insert
- List Append
- List Length
- List Creation
- What is List.
- Locale’s appropriate date and time
- Format Code list of Data, Time and Cal
- Strptime
- Create 12-month Calendar
- Strftime
- Lots of Example on Python Calendar
- Itermonthdates
- Month Calendar
- Time stamp and Date Format
- StrfTime, Now
- Time Delta and UTC
- Time, Hour, Minute, Sec, Microsec
- Date Time
- IsoWeek day
- Day, Month, Year, Today, Weekday
- More example of Python Function
- Map, Filter and Reduce function with Lambda Function
- Programming using functions, modules & external packages
- Random functions in python
- Understanding Packages
- Importing own module as well as external modules
- Organizing python projects into modules
- Organizing python codes using functions
- Recursive Function and Its Advantage and Disadvantage
- Nested Loop in Python Function
- Scope and Life Time of Variable in Python Function
- Help function in Python
- *args and **kwargs
- Lamda/ Anonymous Function
- Write an Empty Function in Python –pass statement.
- Arguments in a function – Required, Default, Positional, Variable-length
- Return Statement
- Function Call
- Python Syntax
- How to use IN or NOT IN keyword in Python Loop.
- Examples of Looping with Break and Continue Statement
- Use If and Else in for and While Loop
- How to use nested Loop in Python
- Generator in Python
- Elegant way of Python Iteration
- Use of Switch Function in Loop
- How to use if and else with Loop
- Programs for printing Patterns in Python
- How to use Range function in Loop
- Looping techniques in Python
- Jumping Statements – Break, Continue, pass
- Loops and Control Statements.
- Loops
- How to use nested IF and Else in Python
- Statements – if, else, elif
- Sets
- Dictionary
- Tuples
- List
- Strings
- Understanding the concept of Casting and Boolean.
- Understanding Non-numeric data types
- Using string data type and string operations
- Declaring and using Numeric data types
- Variable Scope
- Byte objects vs. string in Python
- Type Casting in Python
- Global and Local Variables in Python
- Packing and Unpacking Arguments
- Variables, expression condition and function
- Understating the concepts of Operators
- Identity
- Membership
- Assignment
- Logical
- Relational
- Arithmetic
- Python Indentation
- Python Comments, Multiline Comments.
- Understanding the Python blocks.
- Python basic Operators
- Installation and Working with Python
- Understanding Python variables
Complete Understanding of OS Module of Python
Reading an excel
Contacting user Through Emails Using Python
Python Database Interaction
Python File Handling
Python Exception Handling
Strings
Sets
Dictionary
Tuple
List
Python Date Time and Calendar
Python Function, Modules and Packages
Control Structure & Flow
Python Data Type
Introduction To Variables
Python Keyword and Identiers
Introduction To Python
- Data visualization is the graphical way to representation of information and data. By using visual elements like graphs, maps and charts. Data visualization tools provide an accessible easy way to see and understand the data.
- Customizing Seaborn Plots
- Well done! What’s next
- Bar plot with subgroups and subplots
- Box plot with subgroups
- Rotating x-tics labels
- Putting it all together
- Adding a title and axis labels
- Adding title and labels Part 2
- Adding a title to a face Grid object
- Face Grids vs. Axes Subplots
- Adding titles and labels Part 1
- Using a custom palette
- Changing the scale
- Changing plot style and colour
- Changing style and palette
- Visualizing a Categorical and a Quantitative Variable
- Point plot with subgroups
- Customizing points plots
- Point plots
- Adjusting the whisk
- Omitting outliers
- Create and interpret a box plot
- Box plots
- Customizing bar plots
- Bar plot with percentages
- Count plots
- Current plots and bar plots
- Visualizing Two Quantitative Variables
- Plotting subgroups in line plots
- Interpreting line plots
- Visualizing standard deviation with line plots
- Introduction to line plots
- Changing the style of scatter plot points
- Changing the size of scatter plot points
- Customizing scatters plots
- Creating subplots with col and row
- Introduction to relational plots and subplots
- Introduction to Seaborn
- Hue and scattera plots
- Hue and count plots
- Adding a third variable with hue
- Making a count plot with a Dataframe
- Tidy vs Untidy data
- Making a scatter plot with lists
- Making a count plot with a list
- Using Pandas with seaborn
- Introduction to Seaborn
- MatPlotLib
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Complete Understanding of Histograms
- Other Useful Properties of Charts.
- Create Charts as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Export the Chart as Image
- Play with Charts Properties Using MatPlotLib
- Area Chart using Python MatPlotLib
- Scatter Plot Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Bar Chart using Python MatPlotLib
- NumPy
- NumPy’s Mode, Median and Sum Function
- NumPy’s Sort Function and More
- NumPy’s Mean and Axis
- Indexing NumPy elements using conditionals
- Logical Operation with Arrays
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Two-Dimensional Array
- Operations an Array from a CSV
- Operations with NumPy Arrays
- Creating an Array from a CSV
- NumPy Arrays
- Importing NumPy and Its Properties
- Introduction to NumPy Numerical Python
- Pandas
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
- Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
- Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
- Python | Pandas Data Frame Inner Join
- Under sting the properties of Data Frame Left Join (Left Outer Join)
- Exporting the results to Excel Joins
- Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
- Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
- Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
- Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
- Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Exploring the Data Plotting, Correlations, and Histograms
- Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
- How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
- Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
- Statistics
- Box plot
- Variance
- Standard Deviation
- Correlation
- Interquartile range
- Range
- Mode
- Outliers
- Median
- Numerical Data
- Mean
- Categorical Data
Introduction to Data Visualization with Seaborn
Data Analysis and Visualization using NumPy and MatPlotLib
Data Analysis and Visualization using Pandas.
- Microsoft SQL Server is a relational database management system (RDBMS) that supports a wide variety of transaction processing, business intelligence and analytics applications in corporate IT environments. In order to experiment with data through the creation of test environments, data scientists make use of SQL as their standard tool, and to carry out data analytics with the data that is stored in relational databases like Oracle, Microsoft SQL, MySQL, we need SQL.
- SAVEPOINT and Query Blocking
- AUTOCOMMIT Transaction and usage
- IMPLICIT Transactions and options
- EXPLICIT Transaction types
- ACID Properties and Scope
- KEYSET Cursors with Complex SPs
- FORWARD_ONLY and LOCAL Cursors
- DYNAMIC
- SCROLL Cursors
- STATIC
- Cursor declaration and Life cycle
- Bulk Operations with Triggers
- Database Triggers and Server Triggers
- Data Audit operations & Sampling
- INSERTED and DELETED memory tables
- DML Triggers and Performance impact
- Why to use Triggers
- GROUPING Functions
- Time Functions
- String and Operational Functions
- ROW_COUNT
- Date Functions
- System Functions and usage
- SCHEMABINDING and ENCRYPTION
- Types of Table Valued Functions
- Scalar Valued Functions
- Dynamic SQL and parameterization
- System level Stored Procedures
- INPUT and OUTPUT parameters
- SCHEMABINDING and ENCRYPTION
- Use of Variables and parameters
- Types of Stored Procedures
- Why to use Stored Procedures
- Primary Keys & Non-Clustered Indexes
- Indexes and Table Constraints
- Composite Indexed Columns & Keys
- Materializing Views (storage level)
- INCLUDED Indexes & Usage
- Index SCAN and SEEK
- Indexing Table & View Columns
- Need for Indexes & Usage
- Working with JOINS inside views
- Common Dynamic Management views
- Common System Views and Metadata
- Issues with Views and ALTER TABLE
- Views on Tables and Views
- SCHEMA BINDING and ENCRYPTION
- Benets of Views in SQL Database
- Disabling Constraints & Other Options
- Naming Composite Primary Keys
- CHECK and DEFAULT Constraints
- NULL and IDENTITY properties
- UNIQUE KEY Constraint and NOT NULL
- PRIMARY KEY Constraint & Usage
- Table creation using Constraints
- ROWCOUNT and CUBE Functions
- GROUPING
- HAVING
- Variants of SELECT statement
- ORDER BY
- SELECT queries and Schemas
- Use of WHERE, IN and BETWEEN
- UPDATE
- DELETE
- Table creation using Schemas
- Basic INSERT
- Table Aliases
- Column Aliases & Usage
- Naming Conventions for Columns
- Single Row and Multi-Row Inserts
- Table creation using T-SQL Scripts
- SQL Server Database Tables
- Database Structure modications
- File locations and Size parameters
- DB Design using Files and File Groups
- Database Creation using GUI
- Database Creation using T-SQL scripts
- SQL Database Architecture
- Conventions & Collation
- Conguration Tools & SQLCMD
- Using Management Studio (SSMS)
- SQL Server Features & Purpose
- Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
- SQL Server 2019 Installation
Transactions Management
Cursors and Memory Limitations
Triggers, cursors, memory limitations
System functions and Usage
Stored Procedures and Benets
Indexes and Query tuning
Views and Row Data Security
Data Validation and Constraints
SQL Tables in MS SQL Server
SQL Server 2019 Database Design
SQL Server Fundamentals
- This module offers knowledge to introduce you to the basic principles based on statistical methods and procedures followed in data analysis. This course will help you to understand the work process involved with summarizing the data, data storage, visualizing the data results, and a hands-on approach with statistical analysis with python.
- Probability distribution functions
- Expected value
- Normal distribution
- Gaussian distribution
- Z-score
- Spread and Dispersion
- Correlation and Co-variance
- Sample vs Population Statistics
- Random variables
- Descriptive Statistics
- Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ densi ty etc.)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Descriptive statistics, Frequency Tables and summarization
- Introduction exploratory data analysis
- Label encoding/one hot encoding
- Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
- Feature Selection
- Feature Engineering
- Normalizing data
- Data Manipulation steps (Sorting, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
- Filling missing values using lambda function and concept of Skewness.
- Cleansing Data with Python
- Important python modules Pandas
- Exporting Data to various formats
- Viewing Data objects - sub setting, methods
- Database Input (Connecting to database)
- Importing Data from various sources (Csv, txt, excel, access etc)
- Why Python for data science
- Build Resource plan for Data Science project
- Project plan for Data Science project & key milestones based on effort estimates
- Identify the most appropriate solution design for the given problem statement
- List of steps in Data Science projects
- Data Science Methodology & problem-solving framework.
- Overview of Data Science tools & their popularity.
- Critical success drivers.
- How leading companies are harnessing the power of analytics
- Types of problems and business objectives in various industries
- Relevance in industry and need of the hour
- Classication of data
- What is data
- Common Terms in Data Science
- What is Analytics & Data Science
- Concept of model in analytics and how it is used
- Common terminology used in Analytics & Modelling process
- Popular Modelling algorithms
- Types of Business problems - Mapping of Techniques
- Different Phases of Predictive Modelling
- Need for structured exploratory data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Identify missing data
- Identify outliers’ data
- Imbalanced Data Techniques
- Data Preparation
- Feature Engineering
- Feature Scaling
- Datasets
- Dimensionality Reduction
- Anomaly Detection
- Parameter Estimation
- Data and Knowledge
- Selected Applications in Data Mining
Introduction to Statistics
Data Analysis Visualization Using Python
Feature Engineering in Data Science
Data Manipulation Cleansing - Munging Using Python Modules
Accessing/Importing and Exporting Data
Introduction to Data Analytics
Introduction to Predictive Modelling
EDA (Exploratory Data Analysis)
Data Pre-Processing & Data Mining
- Excel is one of the most popular data analysis tool, to help visualize and gain insights from your data. Analytics with Excel helps you to boost your Microsoft Excel skills.
- Creation of Excel Sheet Data
- Range Name, Format Painter
- Conditional Formatting, Wrap Text, Merge & Centre
- Sort, Filter, Advance Filter
- Different type of Chart Creations
- Auditing, (Trace Precedents, Trace Dependents)Print Area
- Data Validations, Consolidate, Subtotal
- What if Analysis (Data Table, Goal Seek, Scenario)
- Solver, Freeze Panes
- Various Simple Functions in Excel(Sum, Average, Max, Min)
- Real Life Assignment work
- Advance Data Sorting
- Multi-level sorting
- Restoring data to original order after performing sorting
- Sort by icons
- Sort by colours
- Lookup Functions
- Lookup
- VLookup
- HLookup
- Subtotal, Multi-Level Subtotal
- Grouping Features
- Column Wise
- Row Wise
- Consolidation With Several Worksheets
- Filter
- Auto Filter
- Advance Filter
- Printing of Raw & Column Heading on Each Page
- Workbook Protection and Worksheet Protection
- Specified Range Protection in Worksheet
- Excel Data Analysis
- Goal Seek
- Scenario Manager
- Data Table
- Advance use of Data Tables in Excel
- Reporting and Information Representation
- Pivot Table
- Pivot Chat
- Slicer with Pivot Table & Chart
- Generating MIS Report In Excel
- Advance Functions of Excel
- Math & Trig Functions
- Text Functions
- Lookup & Reference Function
- Logical Functions & Date and Time Functions
- Database Functions
- Statistical Functions
- Financial Functions
- Functions for Calculation Depreciation
- Dashboard Background
- Dashboard Elements
- Interactive Dashboards
- Type of Reporting In India
- Reporting Analyst
- Indian Print Media Reporting
- Audit Report
- Accounting MIS Reports
- HR Mis Reports
- MIS Report Preparation Supplier, Exporter
- Data Analysis
- Costing Budgeting Mis Reporting
- MIS Report For Manufacturing Company
- MIS Reporting For Store And Billing
- Product Performance Report
- Member Performance Report
- Customer-Wise Sales Report
- Collections Report
- Channel Stock Report
- Prospect Analysis Report
- Calling Reports
- Expenses Report
- Stock Controller MIS Reporting
- Inventory Statement
- Payroll Report
- Salary Slip
- Loan Assumption Sheet
- Invoice Creation
Understanding Concepts of Excel
Ms Excel Advance
MIS Reporting & Dash Board
- The Power BI course assists the user to understand the way to install Power BI desktop also by understanding and developing the workshop and insights using the data. It offers tools and techniques that are used to visualize and analyze data. The course will help you to learn and grab insights on everything an organization need; to manage the data with Power BI.
- Overview of BI concepts
- Why we need BI
- Introduction to SSBI
- SSBI Tools
- Why Power BI
- What is Power BI
- Building Blocks of Power BI
- Getting started with Power BI Desktop
- Get Power BI Tools
- Introduction to Tools and Terminology
- Dashboard in Minutes
- Interacting with your Dashboards
- Sharing Dashboards and Reports
- Power BI Desktop
- Extracting data from various sources
- Workspaces in Power BI
- Data Transformation
- Query Editor
- Connecting Power BI Desktop to our Data Sources
- Editing Rows
- Understanding Append Queries
- Editing Columns
- Replacing Values
- Formatting Data
- Pivoting and Unpivoting Columns
- Splitting Columns
- Creating a New Group for our Queries
- Introducing the Star Schema
- Duplicating and Referencing Queries
- Creating the Dimension Tables
- Entering Data Manually
- Merging Queries
- Finishing the Dimension Table
- Introducing the another DimensionTable
- Creating an Index Column
- Duplicating Columns and Extracting Information
- Creating Conditional Columns
- Creating the FACT Table
- Performing Basic Mathematical Operations
- Improving Performance and Loading Data into the Data Model
- Introduction to Modelling
- Modelling Data
- Manage Data Relationship
- Optimize Data Models
- Cardinality and Cross Filtering
- Default Summarization & Sort by
- Creating Calculated Columns
- Creating Measures & Quick Measures
- What is DAX
- Data Types in DAX
- Calculation Types
- Syntax, Functions, Context Options
- DAX Functions
- Date and Time
- Time Intelligence
- Information
- Logical
- Mathematical
- Statistical
- Text and Aggregate
- Measures in DAX
- Measures and Calculated Columns
- ROW Context and Filter Context in DAX
- Operators in DAX - Real-time Usage
- Quick Measures in DAX - Auto validations
- In-Memory Processing DAX Performance
- How to use Visual in Power BI
- What Are Custom Visuals
- Creating Visualisations and Colour Formatting
- Setting Sort Order
- Scatter & Bubble Charts & Play Axis
- Tooltips and Slicers, Timeline Slicers & Sync Slicers
- Cross Filtering and Highlighting
- Visual, Page and Report Level Filters
- Drill Down/Up
- Hierarchies and Reference/Constant Lines
- Tables, Matrices & Conditional Formatting
- KPI's, Cards & Gauges
- Map Visualizations
- Custom Visuals
- Managing and Arranging
- Drill through and Custom Report Themes
- Grouping and Binning and Selection Pane, Bookmarks & Buttons
- Data Binding and Power BI Report Server
- Why Dashboard and Dashboard vs Reports
- Creating Dashboards
- Conguring a Dashboard Dashboard Tiles, Pinning Tiles
- Power BI Q&A
- Quick Insights in Power BI
- Custom Data Gateways
- Exploring live connections to data with Power BI
- Connecting directly to SQL Server
- Connectivity with CSV & Text Files
- Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel
- Content packs
- Update content packs
- Introduction and Sharing Options Overview
- Publish from Power BI Desktop and Publish to Web
- Share Dashboard with Power BI Service
- Workspaces (Power BI Pro) and Content Packs (Power BI Pro)
- Print or Save as PDF and Row Level Security (Power BI Pro)
- Export Data from a Visualization
- Export to PowerPoint and Sharing Options Summary
- Understanding Data Refresh
- Personal Gateway (Power BI Pro and 64-bit Windows)
- Replacing a Dataset and Troubleshooting Refreshing
Introduction to Power BI
Power BI Desktop
Power BI Data Transformation
Modelling with Power BI
Data Analysis Expressions (DAX)
Power BI Desktop Visualisations
Introduction to Power BI Dashboard and Data Insights
Direct Connectivity
Publishing and Sharing
Refreshing Datasets
- Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization.
- Data Visualization
- Business Intelligence tools
- Introduction to Tableau
- Tableau Architecture
- Tableau Server Architecture
- VizQL Fundamentals
- Introduction to Tableau Prep
- Tableau Prep Builder User Interface
- Data Preparation techniques using Tableau Prep Builder tool
- Features of Tableau Desktop
- Connect to data from File and Database
- Types of Connections
- Joins and Unions
- Data Blending
- Tableau Desktop User Interface
- Visual Analytics
- Basic Charts Bar Chart, Line Chart, and Pie Chart
- Hierarchies
- Data Granularity
- Highlighting
- Sorting
- Filtering
- Grouping
- Sets
- Types of Calculations
- Built-in Functions (Number, String, Date, Logical and Aggregate)
- Operators and Syntax Conventions
- Table Calculations
- Level of Detail (LOD) Calculations
- Using R within Tableau for Calculations
- Parameters
- Tool tips
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Count Customer by Order
- Profit per Business Day
- Comparative Sales
- Profit Vs Target
- Finding the second order date
- Cohort Analysis
- Introduction to Geographic Visualizations
- Manually assigning Geographical Locations
- Types of Maps
- Spatial Files
- Custom Geocoding
- Polygon Maps
- Web Map Services
- Background Images
- Box and Whisker’s Plot
- Bullet Chart
- Bar in Bar Chart
- Gantt Chart
- Waterfall Chart
- Pareto Chart
- Control Chart
- Funnel Chart
- Bump Chart
- Step and Jump Lines
- Word Cloud
- Donut Chart
- Introduction to Dashboards
- The Dashboard Interface
- Dashboard Objects
- Building a Dashboard
- Dashboard Layouts and Formatting
- Interactive Dashboards with actions
- Designing Dashboards for devices
- Story Points
- Tableau Tips and Tricks
- Choosing the right type of Chart
- Format Style
- Data Visualization best practices
- Publishing Workbooks to Tableau Online
- Interacting with Content on Tableau Online
- Data Management through Tableau Catalog
- AI-Powered features in Tableau Online (Ask Data and Explain Data)
- Understand Scheduling
- Managing Permissions on Tableau Online
- Data Security with Filters in Tableau Online
Introduction to Data Preparation using Tableau Prep
Data Connection with Tableau Desktop
Basic Visual Analytics
Calculations in Tableau
Advanced Visual Analytics
Level of Detail (LOD) Expressions in Tableau
Geographic Visualizations in Tableau
Advanced charts in Tableau
Dashboards and Stories
Get Industry Ready
Exploring Tableau Online
- Many data scientists use R while analyzing data because it has static graphics that produce good-quality data visualizations. Moreover, the programming language has a comprehensive library that provides interactive graphics and makes data visualization and representation easy to analyze.
- History of R
- Advantages and disadvantages
- Downloading and installing
- How to find documentation
- Using the R console and R Studio
- Getting help
- Learning about the environment
- Writing and executing scripts
- Object oriented programming
- Introduction to vectorised calculations
- Introduction to data frames
- Installing and loading packages
- Working directory
- Saving your work
- Variables and assignment
- Data types
- Numeric, character, Boolean, and factors
- Data structures
- Vectors, matrices, arrays, data frames, lists
- Indexing, sub-setting
- Assigning new values
- Viewing data and summaries
- Naming conventions
- Objects
- Built-in data
- Reading data from structured text files
- Reading data using ODBC
- Introduction to tables, enhanced data frames
- Renaming columns
- Adding new columns
- Binning data (continuous to categorical)
- Combining categorical values
- Transforming variables
- Handling missing data
- Merging datasets together
- Stacking datasets together (concatenation)
- Date and date-time classes in R
- Formatting dates for modeling
- Continuous data
- Distributions
- Quantiles, mean
- Bi-modal distributions
- Histograms, box-plots
- Categorical data
- Tables
- Bar plots
- Group by calculations
- Split-apply-combine
- Reshaping and pivoting data in R (long to wide with aggregation)
- Melt and cast
- Finding and matching patterns in text
- Stringer package for text manipulation
- Introduction to regular expressions in R
- Categorical data wrangling with forcats
- Truth testing
- Branching
- Looping
- Functions
- Parameters
- Return values
- Variable scope
- Exception handling
- Applying functions across dimensions
- Sapply, lapply, apply
- Programming with map and purr
- Base graphics system in R
- Scatterplots, histograms, bar charts, box and whiskers, dot plots
- Labels, legends, titles, axes
- Exporting graphics to different formats
- Understanding the grammar of graphics
- Quick plots (qplot function)
- Building graphics by pieces (ggplot function)
- Understanding geoms (geometries)
- Linking chart elements to variable values
- Controlling legends and axes
- Exporting graphics
- Bivariate correlation
- T-test and non-parametric equivalents
- Chi-squared test
- Understanding formulas
- Linear and logistic regression models
- Regression plots
- Confounding / interaction in regression
- Evaluating residuals
- Scoring new data from models (prediction)
- Useful plots from regression models
Overview :
R Programming Basics :
Variable types and data structures in base R :
Getting data into the R environment :
Data frame manipulation :
Handling dates in R :
Exploratory Data Analysis (Descriptive Statistics) :
Working with text data :
Control flow & functions :
Graphics in R Overview :
Advanced R graphics :
Inferential Statistics :
General Linear Regression Models in R :
- AWS allows you to easily move data between the data lake and purpose-built data services. For example, AWS Glue is a serverless data integration service that makes it easy to prepare data for analytics, machine learning, and application development.
- In this module, you will learn what Cloud Computing is and what are the different models of Cloud Computing along with the key differentiators of different models. We will also introduce you to virtual world of AWS along with AWS key vocabulary, services and concepts.
- A Short history
- Client Server Computing Concepts
- Challenges with Distributed Computing
- Introduction to Cloud Computing
- Why Cloud Computing
- Benefits of Cloud Computing
- In this module, you will learn about the introduction to compute offering from AWS called EC2. We will cover different instance types and Amazon AMIs. A demo on launching an AWS EC2 instance, connect with an instance and host ing a website on AWS EC2 instance. We will also cover EBS storage Architecture (AWS persistent storage) and the concepts of AMI and snapshots.
- Amazon EC2
- EC2 Pricing
- EC2 Type
- Installation of Web server and manage like (Apache/ Nginx)
- Demo of AMI Creation
- Exercise
- Hands on both Linux and Windows
- In this module, you will learn how AWS provides various kinds of scalable storage services. In this module, we will cover different storage services like S3, Glacier, Versioning, and learn how to host a static website on AWS.
- Versioning
- Static website
- Policy
- Permission
- Cross region Replication
- AWS-CLI
- Life cycle
- Classes of Storage
- AWS CloudFront
- Real scenario Practical
- Hands-on all above
- In this module, you will learn how to monitoring AWS resources and setting up alerts and notifications for AWS resources and AWS usage billing with AWS CloudWatch and SNS.
- Amazon Cloud Watch
- SNS - Simple Notification Services
- Cloud Watch with Agent
- In this module, you will learn about 'Scaling' and 'Load distribution techniques' in AWS. This module also includes a demo of Load distribution & Scaling your resources horizontally based on time or activity.
- Amazon Auto Scaling
- Auto scaling policy with real scenario based
- Type of Load Balancer
- Hands on with scenario based
- In this module, you will learn introduction to Amazon Virtual Private Cloud. We will cover how you can make public and private subnet with AWS VPC. A demo on creating VPC. We will also cover overview of AWS Route 53.
- Amazon VPC with subnets
- Gateways
- Route Tables
- Subnet
- Cross region Peering
- In this module, you will learn how to achieve distribution of access control with AWS using IAM.
- Amazon IAM
- add users to groups,
- manage passwords,
- log in with IAM-created users.
- User
- Group
- Role
- Policy
- In this module, you will learn how to manage relational database service of AWS called RDS.
- Amazon RDS
- Type of RDS
- RDS Failover
- RDS Subnet
- RDS Migration
- Dynamo DB (No SQL DB)
- Redshift Cluster
- SQL workbench
- JDBC / ODBC
- In this module, you will get an overview of multiple AWS services. We will talk about how do you manage life cycle of AWS resources and follow the DevOps model in AWS. We will also talk about notification and email service of AWS along with Content Distribution Service in this module.
- Cloud Trail,
- In this module, you will cover various architecture and design aspects of AWS. We will also cover the cost planning and optimization techniques along with AWS security best practices, High Availability (HA) and Disaster Recovery (DR) in AWS.
- AWS High Availability Design
- AWS Best Practices (Cost +Security)
- AWS Calculator & Consolidated Billing
- Public DNS
- Private DNS
- Routing policy
- Records
- Register DNS
- Work with third party DNS as well
- Stack
- Templet
- Json / Ymal
- Installation of Linux
- Configuration
- Manage
- Installation of app on Linux (apache / Nginx etc)
- AWS cli configuration on Linux
- Complete hands-on on Linux.
- Scenario based lab and practical
- Each topic and services will be cover with lab and theory.
Introduction to Cloud Computing
Amazon EC2 and Amazon EBS
Amazon Storage Services S3 (Simple Storage Services)
Cloud Watch & SNS
Scaling and Load Distribution in AWS
AWS VPC
Identity and Access Management Techniques (IAM)
Amazon Relational Database Service (RDS)
Multiple AWS Services and Managing the Resources' Lifecycle
AWS Architecture and Design
Migrating to Cloud & AWS
Router S3 DNS
Cloud Formation
Elastic Beanstalk
EFS / NFS (hands-on practice)
Hands-on practice on various Topics
Linux
- Data Scientists know how to train Predictive Models. So, by enabling them to work together, Microsoft Azure Data Science ensures high-quality models at scale in production. With MLOps incorporated as a part of the Microsoft Azure Data Science platform, Data Scientists can create a discrete pipeline for each model
- Identify the benefits and considerations of using cloud services Cloud Computing Basics.
- Identify the benefits of cloud computing, such as High Availability,Scalability, Elasticity,
- Agility, and Disaster Recovery
- Identify the differences between Capital Expenditure (Cap Ex) and Operational.
- Expenditure (Op Ex)
- Describe the consumption-based model
- Describe the differences between categories of cloud services
- Describe the shared responsibility model
- Describe Infrastructure-as-a-Service (IaaS),
- Describe Platform-as-a-Service (PaaS)
- Describe server less computing
- Describe Software-as-a-Service (SaaS)
- Identify a service type based on a use case
- Describe the differences between types of cloud computing
- Define cloud computing
- Describe Public cloud
- Describe Private cloud
- Describe Hybrid cloud
- Compare and contrast the three types of cloud computing Describe Core Azure Services
- Manage Azure AD objects
- create users and groups
- manage user and group properties
- manage device settings
- perform bulk user updates
- manage guest accounts
- configure Azure AD Join
- configure self-service password reset
- NOTE Azure AD Connect; PIM
- Manage role-based access control (RBAC)
- create a custom role
- provide access to Azure resources by assigning roles
- subscriptions
- resource groups
- resources (VM, disk, etc.)
- interpret access assignments
- manage multiple directories
- Manage subscriptions and governance
- configure Azure policies
- configure resource locks
- apply tags
- create and manage resource groups
- move resources
- remove RGs
- manage subscriptions
- configure Cost Management
- configure management groups
- Manage storage accounts
- configure network access to storage accounts
- create and configure storage accounts
- generate shared access signature
- manage access keys
- implement Azure storage replication
- configure Azure AD Authentication for a storage account
- Manage data in Azure Storage
- export from Azure job
- import into Azure job
- install and use Azure Storage Explorer
- copy data by using AZ Copy
- Configure Azure files and Azure blob storage
- create an Azure file share
- create and configure Azure File Sync service
- configure Azure blob storage
- configure storage tiers for Azure blobs
- Configure VMs for high availability and scalability
- configure high availability
- deploy and configure scale sets
- Create and configure VMs
- configure Azure Disk Encryption
- move VMs from one resource group to another
- manage VM sizes
- add data discs
- configure networking
- redeploy VMs
- Create and configure Web Apps
- create and configure App Service
- create and configure App Service Plans
- Implement and manage virtual networking
- create and configure VNET peering
- configure private and public IP addresses, network routes, network interface,
- subnets, and virtual network
- Configure name resolution
- configure Azure DNS
- configure custom DNS settings
- configure a private or public DNS zone
- Secure access to virtual networks
- create security rules
- associate an NSG to a subnet or network interface
- evaluate effective security rules
- deploy and configure Azure Firewall
- deploy and configure Azure Bastion Service
- NOT Implement Application Security Groups; DDoS
- Configure load balancing
- configure Application Gateway
- configure an internal load balancer
- configure load balancing rules
- configure a public load balancer
- troubleshoot load balancing
- NOT Traffic Manager and Front Door and Private Link
- Implement backup and recovery
- configure and review backup reports
- perform backup and restore operations by using Azure Backup Service
- create a Recovery Services Vault
- use soft deletes to recover Azure VMs
Describe Cloud Concepts
Manage Azure identities and governance (15-20%)
Implement and Manage Storage (10-15%)
Deploy and Manage Azure Compute Resources (25-30%)
Configure and Manage Virtual Networking (30-35%)
Monitor and Back up Azure Resources (10-15%)
- The training offers complete career transitioning projects based on the current needs of the organization. These projects are guided by experts and help you to add more value to your profile. You will learn to initiate data analytics projects based on a high-level perspective helping you to understand and articulate the innovative solutions.
- Managing credit card Risks
- Bank Loan default classification
- YouTube Viewers prediction
- Super store Analytics (E-commerce)
- Buying and selling cars prediction (like OLX process)
- Advanced House price prediction
- Analytics on HR decisions
- Survival of the fittest
- Twitter Analysis
- Flight price prediction
Here is the project list you will going to work on
+ More Lessons
Mock Interviews
Projects
Phone (For Voice Call):
+91-971 152 6942WhatsApp (For Call & Chat):
+918287060032self assessment
Learn, Grow & Test your skill with Online Assessment Exam to achieve your Certification Goals
FAQ's
In addition to recorded video courses of lectures, you will get the opportunity to work on actual projects with industry specialists that have more than 10 years of experience at the Data Analyst Training Institute in Noida.
The well-equipped laboratories and the actual project would substantially help the Data Analyst Training in Noida. You'll receive more real-world experience as a consequence, and you'll be better equipped to find a great job.
Croma Campus provides Data Analyst Training & Certification in Noida for you to join in so that you may gain complete skills. The institute's high-quality training will provide you with a diverse set of skills and knowledge.
Because the Data Analyst Course in Noida is customizable, you may do it on your own schedule. However, it will take 30 hours to complete.
Yes, it is one of the most sought-after, and demanding jobs, as there is a need for data analysts to handle the enormous amount of data in various sectors and industries.
In India, an experienced Data Analyst earns Rs. 15.5 Lakhs annually.
Yes, here, we provide training via two methods online and offline, you choose either one as per your wish.
- - Build an Impressive Resume
- - Get Tips from Trainer to Clear Interviews
- - Attend Mock-Up Interviews with Experts
- - Get Interviews & Get Hired
If yes, Register today and get impeccable Learning Solutions!
Training Features
Instructor-led Sessions
The most traditional way to learn with increased visibility,monitoring and control over learners with ease to learn at any time from internet-connected devices.
Real-life Case Studies
Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.
Assignment
Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.
Lifetime Access
Get Unlimited access of the course throughout the life providing the freedom to learn at your own pace.
24 x 7 Expert Support
With no limits to learn and in-depth vision from all-time available support to resolve all your queries related to the course.
Certification
Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.
Training Certification
Your certificate and skills are vital to the extent of jump-starting your career and giving you a chance to compete in a global space.
Talk about it on Linkedin, Twitter, Facebook, boost your resume or frame it- tell your friend and colleagues about it.
Video Reviews
Corporate Training at Croma Campus helped our employees in getting all the required skills through an organized method of instructions. It was truly amazing getting associated with Croma campus with the amazing content delivery and constant support.
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Through corporate training at Croma campus, our employees got all capabilities and knowledge that is required to achieve company goals and success. Furthermore, the training also helped in improving teamwork, employee’s satisfaction, retention, and more.
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Croma Campus is the best place to learn with amazing support and customer services. The team is always ready to help and assist. They helped me to understand all salesforce concept and today I am working as a Salesforce Professional in a leading industry.
Training in Salesforce
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I did complete two courses from Croma Campus. One was web designing and the other was PHP. The mentors are so skillful and knowledgeable. They helped me to learn all concepts with ease without any hassle and I am highly satisfied with my choice.
Training in PHP
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Earlier, I was not sure how online training can help in building a career but the career assistance and mentorship at Croma was so good that I would like to recommend everyone to join the Croma Learning Campus and boost your confidence level too..
Training in Software Testing
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I am a Data Science professional and working with a leading IT industry for more than 15 years. I do have practical exposure to different Data Science services and technologies. I love to teach people having aspiring minds.
Specialization in Data Science
I am a Data Analytics professional with over 10 years of experience. I am associated with Croma campus from last two years. I have trained hundreds of students and guided them towards their career goals and enhancements.
Specialization in Data Analytics
I am a Cloud Computing expert with more than 12 years of experience in an MNC. Till date, I have trained and mentored hundreds of students and professionals globally. Working at Croma is an enriching experience and a wonderful journey too.
Specialization in Cloud Computing
Testimonials & Reviews
I was enrolled for the R programming under the guidance of Mr. Anantha Rao sir. He has a very unique way of teaching and daily presentations by the students were the main highlight of the class. Whatever he taught is actually really he
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Tinish Sharma
TCS R programming
Best training institute for learning Tableau. Anurag Mishra sir is the best trainer for learning Tableau.
Read More...Ishani
Data Analytics
Hi. I have done this course Croma campus. The trainer give the sessions very nicely nd in-depth knowledge. My experience is good throughout the learning program. Management is good. The course helped me in increasing my knowledge and c
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Data Analytics
If you are looking to get a reputed job after SAS certification then you should go for the top which is Croma Campus. I have recently completed my SAS certification and got a placement in a reputed MNC. Moreover, the training offered o
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Data Analytics
Hi, my name is Anup Singh and I have recently completed my SAS certification from Croma Campus. I am fully satisfied after getting a world-class experience at affordable costs. Besides offering out the training they also guided me rega
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Data Analytics
Hi, my name is Manoj Diwakar I have recently completed our Microsoft Excel VBA Certification from Croma Campus Pvt Ltd. It helps out me in clearing the doubts related to training and prepares me for further job interviews.
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Data Analytics
Croma Campus is a very nice training center. I have taken SPSS training in Croma Campus. They are very experienced and the way of teaching is very good. I got the job based on training; it was my great experience at Croma Campus. All t
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Data Analytics
Hi, my name is Neelam. I currently work as a Microsoft Excel at HCL. My interview in this company was scheduled by Croma Campus as I have done 2 months of Microsoft Advance Excel Certification Training course from it. I managed to crac
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