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Learn the fundamental data science concepts. Join today to become a competent data science expert.
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- A Data Science Course in Delhi is a suitable choice for students and fresher, as well as professionals who want to learn about the use of data in organizations. In Croma Campus, students learn some of the essential concepts such as Python, SQL, Statistics, Data Analysis, Data Visualization, and Machine Learning. This training allows learners to learn about the theoretical and practical aspects of data science.
- This Data Science Training Institute in Delhi is also helpful for students who do not have much knowledge about data science. Learners can study step-by-step and practice what they have learned in class through practice. Students can work on datasets, assignments, case studies, and projects to learn more about real-world data operations.
- Training in Croma Campus also covers software tools used for data operation. Students can learn Python to analyze data, SQL for database management and Machine Learning to make predictions. Data Science Coaching in Delhi also includes data visualization where learners can visualize data.
- Another benefit of practical training is that the students not only rely on notes. There are various concepts which need to be practiced and projects that will need to be completed. It will assist in gaining confidence in preparation of jobs.
- Croma Campus Data Science Course in Delhi with Placement offers to eligible candidates. Placement assistance will include the resume preparation, job interviews and job opportunities. But at the same time it is very important to continue your practice and improve yourself since the job depends on the candidate's knowledge, projects and interview performance.
- If you have decided to enroll in a Data Science Training in Delhi then make sure that you consider the syllabus, practical training, projects, trainers and placement assistance before enrolling in the program.
Data Science Course in Delhi
- Data is today being used in almost all types of business organizations. Various businesses have vast amounts of data related to their customers, sales, products and their daily activities. Hence, there are many job opportunities available for individuals who have knowledge of data and are capable of using that knowledge.
- Data Science Course in Delhi gives an overview of the essential skills required to work with the data. The Data Science Training at Croma Campus is specially designed for students, freshers and working professionals who would like to gain knowledge about Data Science right from scratch. The training includes various important subjects such as Python, SQL, Statistics, Data Analysis, Data Visualization and Machine Learning.
- Through assignments, case studies and projects, students will have an opportunity to practice the concepts learnt in class. Students will be able to learn how to process data, derive useful information from it and present findings in a simple manner. Machine Learning will enable the students to understand how computers can be taught from data and predict things.
- The purpose of Data Science Coaching in Delhi is not just to learn new tools. It is equally important for students to know how and where these tools are applied in practical scenarios. Practice on a regular basis will assist the students to develop good skills and become more confident in performing tasks related to data.
- Croma Campus also offers placement assistance to eligible students. This may assist the learner in resume preparation and practice for interviews. However, the students have to continue developing their skills and undertaking projects to prepare themselves for employment in the field of Data Science.
- The primary goal of a Data Science Course in Delhi should be to make the learners appreciate the process through which data is collected, cleansed, analyzed, and utilized to tackle various problems. The course should not be restricted to learning the tools alone. It should make the learners appreciate the practical applications of the tools as well.
- In Croma Campus, Data Science Course in Delhi is geared towards imparting foundational knowledge in various critical components of Data Science, which include:
- The primary objective of Data Science Coaching in Delhi is to ensure that the learners have an understanding of the subject matter known as Data Science. Through practice and projects, they will be able to use these skills as the foundation of their careers in this sector.
Python & SQL Knowledge: Know how Python & SQL are used for manipulating data.
Statistics: Get familiar with basic statistics for data analysis.
Data Manipulation: Acquire knowledge on cleaning, arranging and analyzing various types of data sets.
Data Visualization: Learn about presenting data through various visual forms like tables, graphs and dashboards.
Machine Learning: Understand Machine Learning basics and how models are used to make predictions.
Practical Learning via Projects: Apply knowledge gained during lectures through various practical projects.
Job-oriented Skill Set: Gain basic skills that enable one to undertake basic data science and data analytics jobs.
Problems Solving: Learn how to approach a data-related problem, collect relevant data and present findings.
- Data Science becomes increasingly valuable with the increasing amount of data that is being generated and processed by companies every day. Companies use data for customer understanding, improvement of processes and decision making. That is why the Data Science Training in Delhi may prove to be beneficial for students and specialists wishing to build their career in IT.
- Why Would Data Science Be Helpful in the Future
- Future Skills that Can Help You Succeed in Data Science
- Python and SQL: These are critical skills for data analysis and handling.
- Statistics: Fundamental knowledge of statistics and probabilities will be helpful in data analysis and Machine Learning.
- Communication Skills: Good communication skills are needed in order to present the findings to a non-technical audience.
- Machine Learning and AI: Knowledge of Machine Learning, AI and other related technologies can come in handy as these fields evolve.
- Cloud Skills: Some fundamentals of cloud technology will also be helpful as more and more companies switch to cloud infrastructure.
- The future of Data Science goes beyond learning new tools. It implies problem-solving, data handling, proper usage of AI and the ability to communicate the outcomes effectively. Such students will have a solid foundation for their future career.
Improved Decision Making: The use of data may help companies understand what works and what does not. At the same time, data helps in understanding future tendencies and planning accordingly.
Artificial Intelligence and Automation: Artificial Intelligence and Automation become increasingly relevant in the area of data analysis. Data specialists will have to be proficient at the use of such instruments and at the same time verify whether the data is useful and true.
Used in Multiple Areas: Data Science is not only relevant to IT companies. It is used in banking, healthcare, e-commerce, education, marketing and many other spheres.
Future Data Scientists need to possess an array of technical and fundamental problem-solving skills.
- On getting the Data Science Training Institute in Delhi, your job responsibilities will differ depending on what type of job role you pick. Some people get into working with data and reports, while some get into developing Machine Learning algorithms or data systems.
- The main job role is to work with data, analyze it to extract useful insights and help a business make good decisions. Here, skills such as Python, SQL, Excel, Power BI, Tableau and Machine Learning could prove very helpful.
- Common job roles and responsibilities
- Data Analyst is primarily involved with data to extract valuable information.
- Collect and process data from various sources.
- Perform data verification and identification of patterns and trends.
- Develop reports, visualizations, and dashboards.
- Work with SQL, Excel, Power BI or Tableau software.
- Present valuable insights to the team members.
- Data Scientist deals with more complex and large-scale data issues.
- Collect and process data.
- Analyze data with the use of statistical and Machine Learning methods.
- Develop predictive models based on the analysis results.
- Test and optimize the models.
- Communicate the results of analysis in an understandable way to the business teams.
- Data Engineer works primarily with systems of data storage and movement.
- Design and maintain data pipelines.
- Work with databases and data storage systems.
- Ensure data is delivered to the right destination for analysis.
- Monitor availability of data.
- Support maintenance of data systems by other teams.
- A Machine Learning Engineer works closer with Machine Learning algorithms and software.
- Create and validate Machine Learning algorithms.
- Enhance Machine Learning algorithms' efficiency.
- Implement trained Machine Learning algorithms into applications.
- Monitor Machine Learning algorithms.
- Collaborate with Developers and Data Scientists in AI-powered projects.
- A BI Analyst assists businesses with comprehending their data and reporting.
- Create business reports and dashboards.
- Analyze data for the purpose of deriving business insights.
- Track key performance indicators of the business.
- Possess skillset in Power BI and Tableau tools.
- Support teams to derive conclusions from data.
- Not all students will be hired immediately as a Data Scientist or Machine Learning Engineer after finishing the Data Science Training in Delhi. It will depend on your skills, education, projects, experience, and interviewing ability. It may take some time to build a solid foundation to advance in data positions.
Data Analyst
Data Scientist
Data Engineer
Machine Learning Engineer
Business Intelligence (BI) Analyst
- The objective of the Data Scientist Course in Delhi is to deliver detailed training that covers all the concepts from basic to the advanced level.
The objective of the Data Scientist Course in Delhi is to deliver detailed training that covers all the concepts from basic to the advanced level.
You will learn how to manage different types of data in your organization either it is structured or unstructured so that you could manage complex business requirements that will help you in making a sound decision later.
As the best Data Science Institute in Delhi, we are strongly committed to delivering all essential data science skills right from the beginning to algorithms, data analytics tools, learn complex data models, and also learn to drive meaningful insights from the collected data.
You will get hands-on expertise in using various computer science techniques, perform data visualizations, data analytics, learn R, python programming, and many other useful concepts.
You will gain advanced data science skills too that include AI, Machine Learning, Deep Learning, Big Data Hadoop, Tableau, etc.
This best Data Science Training institute in Delhi will help you to earn all technical skills, analytical skills, and soft skills that will make you confident enough to manage all types of business requirements.
- Based on one survey report submitted by McKinsey Global Institute, companies require 2lac plus skilled resources by 2020 with excellent logical and analytical capabilities. And this demand will definitely go HIGH in the next few years. Anyone who is already working as a data scientist will experience an unexpected hike in his salary. If you also want to become an in-demand IT resource, then take the Data Science Course in Delhi and give new wings to your career right away.
- Being an eminent Data Science Institute in Delhi, we make sure that you could maintain a gradual career growth while enrolled with us and even after the completion of training. Also, it would be great if you could clear one certification exam and gain a hike of a minimum of 30 percent as a certified data science expert.
The average salary of a Data science expert is $139K as per Indeed.
The average salary of a Data science expert is $113K as per Glassdoor.
The average salary of a Data science expert is $100K as per PayScale.
- This Data Science Course in Delhi with Placement covers all essential concepts from an initial level to the advanced stage and makes you future-ready to protect your career even in tough times. It is all because you will gain all practical skills that are important to start your career and this training will make you eligible for top industries too.
- We make you sure that best Data Science Training in Delhi will be a game-changing move for your career where you will find yourself highly competent, skilled, and confident to clear the interviews and get hired. Also, you will gain all related skills to apply for the certification exam and gain credentials that can be shown worldwide. In this way, we will help you to get strong roots in the IT industry and the demand for attractive salary lumps as you desire.
- Data Science is a revolutionary field in the IT industry and an amazing career option too for serious learners. It does not matter either you are a fresher or an experienced learner, best Data Science Course in Delhi with Placement will help you at each step to build a rewarding career that will grow over time. Today, almost every industry generates millions of data Gigs daily and it is vital to analyze these data files thoroughly to drive meaningful insights from it. This is the reason why the demand for Data science professionals is increasing every day that have the right skill set to perform efficiently at the workplace.
- The implementation of Data Science techniques is not limited to the IT industry but you can work in other industry verticals too like healthcare, education sector, telecom sector, the insurance industry, and more. So, it is one of the most lucrative career options that witness HUGE salary lumps for skilled people. And it would be great to see how data science can change the world around us in the future.
- So, it is necessary to get an insight into important facts why Data Science is considered the best career option in 2020 and beyond.
- As the best Data Science Training Institute in Delhi, we assure you to deliver all essential skills and also helps you to start a career in the data science space with a BANG. Today, when so many industries are looking for data science experts, the Data Science Course in Delhi can give new wings to your career to fly higher.
#1 Skill in the IT industry with an average salary of $106K per year.
190K jobs are predicted by the next year.
The Data Science industry is expected to touch $16 Billion by 2025
Thousands of students have already taken the Data Science Course and placed with Top MNC
Almost every IT industry is looking for skilled Data Science professionals.
- The top hiring industries in Data Science include IBM, Accenture, Amazon, Flipkart, Snapdeal, Microsoft, Google, Infosys, NETFLIX, FEDEX, American Express, and more. And the Industry-Domain you can work in, as a Data Scientist on the successful completion of Data Science Training in Delhi includes, IT Sector, Healthcare and Medical Sector, Banking & Finance, Transportation, Travel Industry, eCommerce, Media & Entertainment, Non-Profit Industries, Insurance Sector, etc.
- The number of options is really wider for skilled professionals and our Data Science Coaching in Delhi prepares you for all the top industries and various industry verticals too. We ensure that you will feel confident about your skills, on the course completion, and clear all your interviews in the first attempt.
- Also, get complete placement assistance, resume building guide, and learn all important tips to prepare for an interview. We also do provide a set of top data Science interview questions to evaluate your skills.
- On the successful completion of the Data Science Certification Course, you will get a training certificate that is valid around the world and clear proof of your skills. If you attempt the global certification exam in Data Science, it will make you a more demanding resource when compared to other people who applied for the job.
- It does not matter either you start as a fresher or experienced professional who wants to switch to the Data Science domain, it is always recommended moving forward with a thoughtful career plan in your mind.
- Let us learn to swim in the Data science pool and establish yourself as a competent It resource across the globe by completing a Data Scientist Course in Delhi with the top industry leaders in the education sector.
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CURRICULUM & PROJECTS
Data Science Training Program
- 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.
- 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 Reverse
- 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 Syntax
- Function Call
- Return Statement
- Arguments in a function - Required, Default, Positional, Variable-length
- Write an Empty Function in Python -pass statement.
- 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
- Creation and working of decorator
- Idea and practical example of generator, use of generator
- Concept and working of Iterator
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specific 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
- Threading, Multi-threading
- Memory management concept of python
- working of Multi tasking system
- Different os function with thread
- SQL Database connection using
- Creating and searching tables
- Reading and Storing config information on database
- Programming using database connections
- Working With Excel
- Reading an excel file using Python
- Writing to an excel sheet using Python
- Python| Reading an excel file
- Python | Writing an excel file
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel file.
- 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 profile detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any files and folder's details using OS
- PandasAI: Natural language queries on DataFrames
- OpenAI API (GPT) to generate code, EDA, and reports
- LangChain for building chat-based data apps
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- Statistics
- Pandas
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- 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
- 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, Feature Transformation and Dimensionality Reduction
- Datasets
- Dimensionality Reduction (PCA, ICA,LDA)
- Anomaly Detection
- Parameter Estimation
- Data and Knowledge
- Selected Applications in Data Mining
- Difference between Analysis and Analytics
- Concept of model in analytics and how it is used
- Common terminology used in Analytics & Modelling process
- Popular Modelling algorithms, Data Analytics Life cycle
- Types of Business problems - Mapping of Techniques
- Introduction to Machine Learning
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- SQL Server 2019 Installation
- Service Accounts & Use, Authentication Modes & Usage, Instance Configurations
- SQL Server Features & Purpose
- Using Management Studio (SSMS)
- Configuration 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 modifications
- 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
- AI Tools:
- Copilot in Azure Data Studio or GitHub for SQL suggestions
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- 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
- Subtotal, Multi-Level Subtotal
- Grouping Features
- Consolidation With Several Worksheets
- Filter
- Printing of Raw & Column Heading on Each Page
- Workbook Protection and Worksheet Protection
- Specified Range Protection in Worksheet
- Excel Data Analysis
- Data Table
- Pivot Table
- Generating MIS Report In Excel
- Text Functions
- Lookup & Reference Function
- Logical Functions & Date and Time Functions
- Database Functions
- Statistical Functions
- Financial Functions
- Functions for Calculation Depreciation
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- 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
- 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
- Smart Narrative visual (AI-generated insights)
- Decomposition Tree (Root Cause Analysis)
- Q&A Visual (Natural Language Querying)
- Azure Cognitive Services integration
- Power BI Copilot (Preview): Report creation via prompts
- Integration with Power Automate for alerts and workflows
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- 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 Whiskers 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
- Ask Data: Natural Language Data Exploration
- Explain Data: Automatic statistical insights
- Tableau GPT (Einstein Copilot - Salesforce)
- AI forecasting in visualizations
- Integration with Python (TabPy) and R
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- What is Machine Learning
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- What is Time Series Analysis
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF
- What is Exploratory Data Analysis
- EDA Techniques
- EDA Classification
- Univariate Non-graphical EDA
- Univariate Graphical EDA
- Multivariate Non-graphical EDA
- Multivariate Graphical EDA
- Heat Maps
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
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- 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
- Understand The Inner Workings Of Neural Networks And Train Them With Keras.
- Introduction to Neural Networks & Deep Learning
- Activation Functions (ReLU, Sigmoid, Tanh)
- Feedforward Neural Network
- Fully Connected Layer Forward,Backward Pass
- Regularization Dropout, Batch Normalization
- Data Preprocessing & Data Augmentation
- Babysitting Learning: Overfit detection, TensorBoard Monitoring
- Master CNNs, Object Detection, Segmentation, And Deployment.
- Basics of Images, Image Preprocessing
- Convolution: 2D Conv, Forward & Backward
- Pooling, Padding, Stride, Transposed Conv
- CNN Architectures: LeNet, AlexNet, VGG, ResNet
- GPU vs CPU for DL
- Transfer Learning: Inception, MobileNet, fine-tuning
- Semantic Segmentation using UNet
- Object Detection YOLO, SSD, Region Proposal
- Bounding Box Regressor
- Siamese Networks for Similarity Search
- Image Classification with CNN
- Object Detection with YOLOv8
- Visual Search with Embeddings
- Train Text Models From Scratch And With BERT.
- Introduction to NLP and Use Cases
- Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
- Feature Extraction: BOW, TF-IDF, N-Grams
- Word Embeddings: Word2Vec, GloVe, Dense Vectors
- POS Tagging, Named Entity Recognition
- RNN, LSTM Forward Pass and BPTT
- Advanced LSTM Applications + Architectures
- Attention Mechanism + Encoder-Decoder
- Transformers, BERT, Hugging Face Pipelines
- NLP Evaluation Metrics: BLEU, ROUGE
- Sentiment Classifier (LSTM or BERT)
- Deploy NLP Model with Streamlit
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