Learn the fundamental data science concepts. Join today to become a competent data science expert.
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Corporate Served- Data Science is a dynamic analytics field that empowers professionals to act as business protectors, providing valuable insights for companies. At Croma Campus, we understand the high demand for data scientists globally, making it a promising career choice for dedicated learners in Bangalore.
- Whether you opt for self-study or a paid program, consider our classes for Data Science in Bangalore. As the top Data Science training institute in Bangalore, we are committed to helping you achieve success as a Data Scientist, offering substantial earning potential in various industries.
Data Science Course in Bangalore
- Our Data Science courses at Croma Campus in Bangalore are designed with a comprehensive approach, covering fundamental concepts and advanced data analytics. The primary objectives include:
Data Proficiency: Mastering structured and unstructured data handling for various business contexts.
Informed Decision-Making: Using data effectively to make strategic decisions that drive organizational success.
Full Skill Set: Equipping students with comprehensive data science expertise, from tools and algorithms to complex data modeling, business needs analysis, and in-depth data analysis.
Broad Learning: Covering statistics, computer science, data analytics, data visualization, and programming languages like R and Python.
Advanced Concepts: Exploring big data technologies, machine learning, artificial intelligence, and deep learning.
- Delve into the salary expectations for fresh graduates pursuing a data science career in Bangalore:
- These figures underscore the lucrative nature of data science careers, making them an attractive option for professionals starting or transitioning into this field.
Salary Landscape: The average salary of a data science expert stands at $139,000, as reported by Indeed.
Salary Insights: Glassdoor estimates the average salary for data science experts at $113,000.
Market Trends: According to PayScale, data science professionals earn around $100,000 on average.
- Our Data Science courses at Croma Campus cater to a diverse audience in Bangalore, including:
- This diversity underscores the inclusivity of Data Science Course, making them accessible to all driven by the passion for success in this dynamic field.
Undergraduate and Postgraduate Students: Enthusiastic learners looking to kickstart a rewarding career in the field of Data Scientist Classes.
IT Professionals: Experienced IT experts looking to upgrade their skills and seize data science opportunities.
Business Professionals: Executives and decision-makers eager to enhance their data analysis capabilities.
Academics and Researchers: Scholars and researchers aiming to explore data science as an academic or research specialization.
Professionals Seeking a Career Change: Individuals from various backgrounds contemplating a transition into data science for a fresh career trajectory.
- With intensive training covering a broad spectrum of data science topics, individuals can anticipate remarkable career growth upon completing our data science classes in Bangalore. Students are empowered to:
- These outcomes emphasize the broad spectrum of career advancement possibilities offered by our data science training in Bangalore, making it an appealing field for individuals seeking growth and versatility in their professional journey.
Apply Practical Knowledge: Implement acquired concepts in real-world scenarios, effectively addressing complex challenges.
Earn Certifications: Attain certifications that enhance employability and earning potential.
Secure Job Opportunities: Unlock a multitude of job openings across diverse industries and organizations.
Contribute to Industry: Play a pivotal role in the transformation of various sectors through data-driven insights and decision-making.
Obtain Leadership Positions: Possess the competence to advance into leadership roles, such as Data Science Manager, or other technical positions like Machine Learning Engineer or Data Architect.
- The future of data science is exceedingly promising, with data professionals set to play a pivotal role in harnessing the power of data across various industries in Bangalore. Key trends shaping the future scope of data science include:
- This dynamic environment is set to create exciting opportunities for data professionals in Bangalore in the years to come, making data science a field worth investing in for a bright future.
Growing Demand: The increasing reliance on data in organizations will continue to drive demand for data professionals.
Emerging Technologies: The convergence of data science with emerging technologies like IoT, Blockchain, and AI promises diverse job roles and opportunities.
Varied Job Roles: Data science professionals will have the option to explore diverse job profiles and industries.
Evolving Data Landscape: As organizations increasingly rely on data, the field of data science will adapt to changing data landscapes.
- As data science gains prominence, numerous industries in Bangalore actively seek professionals with data science expertise. The top hiring industries in data science include:
- As a leading data science training institute in Bangalore, Croma Campus prepares students to excel in these industries, providing a competitive edge in securing sought-after positions.
IT Sector: Leading tech companies like IBM, Microsoft, Google, and Infosys consistently seek skilled data scientists.
Healthcare and Medical Sector: The healthcare industry relies on data for decision-making, creating a high demand for data professionals.
Banking & Finance: Financial institutions leverage data science for risk assessment, fraud detection, and customer insights.
Transportation: Data science is essential for optimizing routes, managing logistics, and improving transportation services.
Travel Industry: The travel sector employs data science for recommendations, personalization, and enhancing the customer experience.
eCommerce: Companies like Amazon, Flipkart, and Snapdeal employ data scientists to enhance user experiences and boost sales.
Media & Entertainment: Data analysis plays a pivotal role in content recommendation, user engagement, and personalization.
Non-Profit Industries: Non-profit organizations utilize data science to drive social impact and optimize operations.
Insurance Sector: Data science helps insurers analyze risks, set premiums, and predict claims effectively.
- Embarking on a data science career opens doors to a plethora of exciting job profiles in the data-driven industry, each offering unique challenges and opportunities. Let's explore some of the prominent job roles and their corresponding salary expectations in the world of data science in Bangalore:
Data Science Classes: Data scientists work with complex datasets, uncovering patterns and trends to derive actionable insights. Their average salary stands at $139,000, as per Indeed, making it a highly rewarding profession.
Data Engineer: Data engineers design, construct, install, and maintain systems for data generation. Their role is pivotal in ensuring data availability and quality. They typically earn competitive salaries with growth potential.
Statistician: Statisticians play a vital role in analyzing data to generate valuable statistics, providing organizations with crucial insights. Their skills are in high demand, and they receive substantial compensation.
Business Intelligence Analyst: Business intelligence analysts focus on interpreting data to support informed decision-making in a company. Their proficiency in data-driven insights contributes to their attractive salaries.
Machine Learning Engineer: Machine learning engineers develop algorithms and models that power artificial intelligence applications. Their role is crucial in the development of AI solutions, making it a high-paying profession.
Data Science Manager: Data science managers oversee data science projects, ensuring teams work efficiently and effectively.
- After finishing the Data Science Certification Course at Croma Campus, you'll receive a training certificate. To earn it, you need to complete projects and tasks, with your skills checked at various times.
- It's a wise move to have a career plan, no matter where you are in your IT career. So, consider our thorough data science training in Bangalore, and don't wait any longer.
- You May Also Read:
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Data Science Certification Training
- Data Science 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 Science with Machine Learning, and Deep Learning (including Data Analytics & Cloud Implementation) which includes a 6 months online project internship.
- Python for Data Science
- Data Analysis and Visualization
- Databases – MS SQL and SQL Queries
- Statistics for Data Science
- Mastering Machine Learning
- Understanding Deep Learning
- Microsoft Power BI
- Mastering Tableau
- Cloud: AWS(Amazon Web Services)
- Cloud: Microsoft Azure Fundamentals
- Data Science - Live Projects
In this program you will learn:
- Data Science 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.
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- Variables, expression condition and function
- Global and Local Variables in Python
- Packing and Unpacking Arguments
- Type Casting in Python
- Byte objects vs. string in Python
- Variable Scope
- Declaring and using Numeric data types
- Using string data type and string operations
- Understanding Non-numeric data types
- Understanding the concept of Casting and Boolean.
- Strings
- List
- Tuples
- Dictionary
- Sets
- Statements – if, else, elif
- How to use nested IF and Else in Python
- Loops
- Loops and Control Statements.
- Jumping Statements – Break, Continue, pass
- Looping techniques in Python
- How to use Range function in Loop
- Programs for printing Patterns in Python
- How to use if and else with Loop
- Use of Switch Function in Loop
- Elegant way of Python Iteration
- Generator in Python
- How to use nested Loop in Python
- Use If and Else in for and While Loop
- Examples of Looping with Break and Continue Statement
- How to use IN or NOT IN keyword in Python Loop.
- Python Syntax
- Function Call
- Return Statement
- Arguments in a function – Required, Default, Positional, Variable-length
- Write an Empty Function in Python –pass statement.
- Lamda/ Anonymous Function
- *args and **kwargs
- Help function in Python
- Scope and Life Time of Variable in Python Function
- Nested Loop in Python Function
- Recursive Function and Its Advantage and Disadvantage
- Organizing python codes using functions
- Organizing python projects into modules
- Importing own module as well as external modules
- Understanding Packages
- Random functions in python
- Programming using functions, modules & external packages
- Map, Filter and Reduce function with Lambda Function
- More example of Python Function
- Day, Month, Year, Today, Weekday
- IsoWeek day
- Date Time
- Time, Hour, Minute, Sec, Microsec
- Time Delta and UTC
- StrfTime, Now
- Time stamp and Date Format
- Month Calendar
- Itermonthdates
- Lots of Example on Python Calendar
- Create 12-month Calendar
- Strftime
- Strptime
- Format Code list of Data, Time and Cal
- Locale’s appropriate date and time
- What is List.
- List Creation
- List Length
- List Append
- List Insert
- List Remove
- List Append & Extend using “+” and Keyword
- List Delete
- List related Keyword in Python
- List Revers
- List Sorting
- List having Multiple Reference
- String Split to create a List
- List Indexing
- List Slicing
- List count and Looping
- List Comprehension and Nested Comprehension
- What is Tuple
- Tuple Creation
- Accessing Elements in Tuple
- Changing a Tuple
- Tuple Deletion
- Tuple Count
- Tuple Index
- Tuple Membership
- TupleBuilt in Function (Length, Sort)
- Dict Creation
- Dict Access (Accessing Dict Values)
- Dict Get Method
- Dict Add or Modify Elements
- Dict Copy
- Dict From Keys.
- Dict Items
- Dict Keys (Updating, Removing and Iterating)
- Dict Values
- Dict Comprehension
- Default Dictionaries
- Ordered Dictionaries
- Looping Dictionaries
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specic Exception in Python
- Raising Exception
- Try and Finally
- Opening a File
- Python File Modes
- Closing File
- Writing to a File
- Reading from a File
- Renaming and Deleting Files in Python
- Python Directory and File Management
- List Directories and Files
- Making New Directory
- Changing Directory
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- Installing SMTP Python Module
- Sending Email
- Reading from le and sending emails to all users
- Working With Excel
- Reading an excel le using Python
- Writing to an excel sheet using Python
- Python| Reading an excel le
- Python | Writing an excel le
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel le.
- Play with Workbook, Sheets and Cells in Excel using Python
- Creating and Removing Sheets
- Formatting the Excel File Data
- More example of Python Function
- Check Dirs. (exist or not)
- How to split path and extension
- How to get user prole detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any les and folder’s details using OS
Introduction To Python
Python Keyword and Identiers
Introduction To Variables
Python Data Type
Control Structure & Flow
Python Function, Modules and Packages
Python Date Time and Calendar
List
Tuple
Dictionary
Sets
Strings
Python Exception Handling
Python File Handling
Python Database Interaction
Contacting user Through Emails Using Python
Reading an excel
Complete Understanding of OS Module of 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.
- Statistics
- Categorical Data
- Numerical Data
- Mean
- Median
- Mode
- Outliers
- Range
- Interquartile range
- Correlation
- Standard Deviation
- Variance
- Box plot
- Pandas
- Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
- How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
- Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
- Exploring the Data Plotting, Correlations, and Histograms
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
- Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
- Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
- Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
- Exporting the results to Excel Joins
- Python | Pandas Data Frame Inner Join
- Under sting the properties of Data Frame Left Join (Left Outer Join)
- Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
- Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
- Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- NumPy
- Introduction to NumPy Numerical Python
- Importing NumPy and Its Properties
- NumPy Arrays
- Creating an Array from a CSV
- Operations an Array from a CSV
- Operations with NumPy Arrays
- Two-Dimensional Array
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Logical Operation with Arrays
- Indexing NumPy elements using conditionals
- NumPy’s Mean and Axis
- NumPy’s Mode, Median and Sum Function
- NumPy’s Sort Function and More
- MatPlotLib
- Bar Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Area Chart using Python MatPlotLib
- Scatter Plot Chart using Python MatPlotLib
- Play with Charts Properties Using MatPlotLib
- Export the Chart as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Create Charts as Image
- Other Useful Properties of Charts.
- Complete Understanding of Histograms
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Seaborn
- Introduction to Seaborn
- Making a scatter plot with lists
- Making a count plot with a list
- Using Pandas with seaborn
- Tidy vs Untidy data
- Making a count plot with a Dataframe
- Adding a third variable with hue
- Hue and scattera plots
- Hue and count plots
- Visualizing Two Quantitative Variables
- Introduction to relational plots and subplots
- Creating subplots with col and row
- Customizing scatters plots
- Changing the size of scatter plot points
- Changing the style of scatter plot points
- Introduction to line plots
- Interpreting line plots
- Visualizing standard deviation with line plots
- Plotting subgroups in line plots
- Visualizing a Categorical and a Quantitative Variable
- Current plots and bar plots
- Count plots
- Bar plot with percentages
- Customizing bar plots
- Box plots
- Create and interpret a box plot
- Omitting outliers
- Adjusting the whisk
- Point plots
- Customizing points plots
- Point plot with subgroups
- Customizing Seaborn Plots
- Changing plot style and colour
- Changing style and palette
- Changing the scale
- Using a custom palette
- Adding titles and labels Part 1
- Face Grids vs. Axes Subplots
- Adding a title to a face Grid object
- Adding title and labels Part 2
- Adding a title and axis labels
- Rotating x-tics labels
- Putting it all together
- Box plot with subgroups
- Bar plot with subgroups and subplots
- Well done! What’s next
Data Analysis and Visualization using Pandas.
Data Analysis and Visualization using NumPy and MatPlotLib
Introduction to Data Visualization with Seaborn
- 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.
- SQL Server 2019 Installation
- Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
- SQL Server Features & Purpose
- Using Management Studio (SSMS)
- Conguration Tools & SQLCMD
- Conventions & Collation
- SQL Database Architecture
- Database Creation using GUI
- Database Creation using T-SQL scripts
- DB Design using Files and File Groups
- File locations and Size parameters
- Database Structure modications
- SQL Server Database Tables
- Table creation using T-SQL Scripts
- Naming Conventions for Columns
- Single Row and Multi-Row Inserts
- Table Aliases
- Column Aliases & Usage
- Table creation using Schemas
- Basic INSERT
- UPDATE
- DELETE
- SELECT queries and Schemas
- Use of WHERE, IN and BETWEEN
- Variants of SELECT statement
- ORDER BY
- GROUPING
- HAVING
- ROWCOUNT and CUBE Functions
- Table creation using Constraints
- NULL and IDENTITY properties
- UNIQUE KEY Constraint and NOT NULL
- PRIMARY KEY Constraint & Usage
- CHECK and DEFAULT Constraints
- Naming Composite Primary Keys
- Disabling Constraints & Other Options
- Benets of Views in SQL Database
- Views on Tables and Views
- SCHEMA BINDING and ENCRYPTION
- Issues with Views and ALTER TABLE
- Common System Views and Metadata
- Common Dynamic Management views
- Working with JOINS inside views
- Need for Indexes & Usage
- Indexing Table & View Columns
- Index SCAN and SEEK
- INCLUDED Indexes & Usage
- Materializing Views (storage level)
- Composite Indexed Columns & Keys
- Indexes and Table Constraints
- Primary Keys & Non-Clustered Indexes
- Why to use Stored Procedures
- Types of Stored Procedures
- Use of Variables and parameters
- SCHEMABINDING and ENCRYPTION
- INPUT and OUTPUT parameters
- System level Stored Procedures
- Dynamic SQL and parameterization
- Scalar Valued Functions
- Types of Table Valued Functions
- SCHEMABINDING and ENCRYPTION
- System Functions and usage
- Date Functions
- Time Functions
- String and Operational Functions
- ROW_COUNT
- GROUPING Functions
- Why to use Triggers
- DML Triggers and Performance impact
- INSERTED and DELETED memory tables
- Data Audit operations & Sampling
- Database Triggers and Server Triggers
- Bulk Operations with Triggers
- Cursor declaration and Life cycle
- STATIC
- DYNAMIC
- SCROLL Cursors
- FORWARD_ONLY and LOCAL Cursors
- KEYSET Cursors with Complex SPs
- ACID Properties and Scope
- EXPLICIT Transaction types
- IMPLICIT Transactions and options
- AUTOCOMMIT Transaction and usage
- SAVEPOINT and Query Blocking
SQL Server Fundamentals
SQL Server 2019 Database Design
SQL Tables in MS SQL Server
Data Validation and Constraints
Views and Row Data Security
Indexes and Query tuning
Stored Procedures and Benets
System functions and Usage
Triggers, cursors, memory limitations
Cursors and Memory Limitations
Transactions Management
- 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.
- What is Analytics & Data Science
- Common Terms in Data Science
- What is data
- Classication of data
- Relevance in industry and need of the hour
- Types of problems and business objectives in various industries
- How leading companies are harnessing the power of analytics
- Critical success drivers.
- Overview of Data Science tools & their popularity.
- Data Science Methodology & problem-solving framework.
- List of steps in Data Science projects
- Identify the most appropriate solution design for the given problem statement
- Project plan for Data Science project & key milestones based on effort estimates
- Build Resource plan for Data Science project
- Why Python for data science
- Importing Data from various sources (Csv, txt, excel, access etc)
- Database Input (Connecting to database)
- Viewing Data objects - sub setting, methods
- Exporting Data to various formats
- Important python modules Pandas
- Cleansing Data with Python
- Filling missing values using lambda function and concept of Skewness.
- Data Manipulation steps (Sorting, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
- Normalizing data
- Feature Engineering
- Feature Selection
- Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
- Label encoding/one hot encoding
- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ densi ty etc.)
- Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
- Descriptive Statistics
- Sample vs Population Statistics
- Random variables
- Probability distribution functions
- Expected value
- Normal distribution
- Gaussian distribution
- Z-score
- Spread and Dispersion
- Correlation and Co-variance
- 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 Data Science
Accessing/Importing and Exporting Data
Data Manipulation Cleansing - Munging Using Python Modules
Feature Engineering in Data Science
Data Analysis Visualization Using Python
Introduction to Statistics
Introduction to Predictive Modelling
EDA (Exploratory Data Analysis)
Data Pre-Processing & Data Mining
- Machine learning courses help to understand the complete concepts behind the processing of Artificial intelligence and Computer science. With the Machine learning course, you will cover topics based on supervised and unsupervised learning along with the development of software and algorithms to extract predictions based on data.
- Machine Learning
- Machine Learning Algorithms
- Algorithmic models of Learning
- Applications of Machine Learning
- Large Scale Machine Learning
- Computational Learning theory
- Supervised Learning
- Unsupervised Learning
- Semi-supervised and Reinforcement Learning
- Bias and variance Trade-off
- Representation Learning
- Regression and its Types
- Logistic Regression
- Linear Regression
- Polynomial Regression
- Meaning and Types of Classication
- Probability and Bayes Theorem
- Support Vector Machines
- Naive Bayes
- Decision Tree Classier
- Random Forest Classier
Introduction to Machine Learning
Techniques of Machine Learning
Regression
Classication
- Deep learning is the most effective skill in AI. The course is intended to provide a complete foundation over the deep learning algorithms that help you to understand the process to build neural networks. The course of deep learning will help you to successfully handle the Machine learning projects needed by the organization today.
- What are the Limitations of Machine Learning
- What is Deep Learning
- Advantage of Deep Learning over Machine learning
- Reasons to go for Deep Learning
- Real-Life use cases of Deep Learning
- What is Deep Learning Networks
- Why Deep Learning Networks
- How Deep Learning Works
- Feature Extraction
- Working of Deep Network
- Training using Backpropagation
- Variants of Gradient Descent
Introduction to Deep Learning
Deep Learning Networks
- 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
- 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 science projects based on a high-level perspective helping you to understand and articulate the innovative solutions for topical real-time data science projects.
- 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
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FAQ's
Croma Campus offers comprehensive Data Scientist Course In Bangalore with expert guidance and hands-on experience, ensuring you are job-ready.
Upon course completion, you'll have proficiency in data handling, analysis, and advanced data science concepts, setting you up for a successful career.
No, there are no specific prerequisites for joining our Data Scientist Course In Bangalore. We welcome learners from various backgrounds.
Your earning potential is significant, dependent on your skill level and location.
Yes, we provide placement support to help you secure a job and kick-start your data science career.
- - 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.
Showcase your Course Completion Certificate to Recruiters
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Training Certificate is Govern By 12 Global Associations.
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Training Certificate is Powered by “Wipro DICE ID”
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Training Certificate is Powered by "Verifiable Skill Credentials"
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