Requirements

Basic 12th pass or equivalent education (any stream)
Comfortable with using a computer and the internet
Basic math skills (like percentages, averages, and simple statistics)
Willingness to learn tools like Excel, SQL, Power BI, and Python
Curiosity for data and problem-solving mindset
No prior programming experience needed (if beginners course)
Laptop or desktop with good internet connection for online learning

Features

Live Project-Based Training
Expert-Led Sessions
Flexible Learning Options
Interactive Learning
Smart Labs with Advanced Equipment
Unlimited Lab Access
Comprehensive Study Material
Globally Recognized Certification
One-on-One Mentorship
Career Readiness
Job Assistance

Target audiences

Students after 12th and college learners looking for job-oriented skills
Fresh graduates from any stream (Commerce, Science, Arts, IT, Management)
Working professionals seeking career growth and salary improvement
IT professionals planning to move into analytics and business roles
Non-technical background learners entering the tech industry
Career switchers looking for high-demand job opportunities
MBA students and management professionals enhancing decision-making skills
Entrepreneurs and business owners using data for business growth
Job seekers preparing for placements and interviews
Freelancers and remote job aspirants building analytics portfolios

Data Analytics Course | Data Analytics Training

The Data Analytics Course by Ascents Learning is a practical program that teaches how to collect, clean, analyze,
and present data for business decisions. You learn the core workflow used in most analyst roles: Excel-based reporting, SQL querying, dashboard building, and basic analytics reasoning.

This Data Analytics Training is designed for students, freshers, working professionals, and career switchers.
If you’re starting from scratch, the learning path is structured and step-by-step. If you already work with reports or spreadsheets,the course helps you move beyond manual reporting into SQL-driven analysis and BI dashboards.

By the end, learners should be able to build a portfolio that includes dashboards, SQL analysis tasks, and a capstone project.
For learners looking for a Data Analytics Course with Placement support, Ascents Learning also provides interview preparation, resume/LinkedIn guidance, and portfolio review.

Course Overview

This Data Analytics Course covers the full analytics cycle—from raw data to business insight. The focus stays on real tasks
you’ll handle in an analyst job, not just tool demos.

What the course typically includes:

Working with structured datasets (sales, customer, marketing, operations, finance-style reporting)
Cleaning and preparing data for analysis
Writing SQL queries for reporting and analysis
Building dashboards using Power BI/Tableau concepts
Presenting insights clearly (what happened, why it happened, what to do next)
Capstone project based on a real business case

This Data Analytics Training is designed to help learners build repeatable skills that carry over to different industries.

Who Should Enroll

This Data Analytics Course is a good fit for:

Students (UG/PG): who want a job-ready skill and a portfolio
Freshers: targeting entry-level data analyst, MIS, reporting, or BI roles
Working professionals: who deal with reporting, KPIs, or operational data and want better analysis skills
Career switchers: moving into analytics from non-tech backgrounds

You’ll benefit most if you:

Want a structured learning path instead of scattered tutorials
Are willing to practice regularly (assignments matter in analytics)
Want to build projects that look real on a resume

If you’re comparing providers for the Best Data Analytics Training, focus on project quality, SQL depth, and dashboard practice—those are the skills companies test.

Learning Outcomes

Work confidently with data

Clean messy datasets (duplicates, missing values, inconsistent formats)
Build a simple preparation process you can repeat

Use Excel for analysis and reporting

Pivot Tables and Pivot Charts
Lookups (XLOOKUP/VLOOKUP), IF logic, SUMIFS/COUNTIFS
KPI reporting and charts
Basic Power Query-style cleaning (where included)

Query data using SQL

SELECT, WHERE, ORDER BY, GROUP BY, HAVING
JOINs (INNER/LEFT) and aggregations
Reporting-style outputs and basic subqueries

Build dashboards and reports

KPI cards, trend charts, filters/slicers, drilldowns
Dashboard layout and stakeholder-friendly reporting
Power BI data modeling basics and DAX foundations (where included)

Communicate insights clearly

Translate analysis into decisions and next steps
Explain results without jargon

A strong Data Analytics Course with Placement support is not just about interviews—it’s about having work you can show.
This course is structured to help you produce that work.

Teaching Methodology

Ascents Learning runs this Data Analytics Course with a practical-first approach, so learners gain confidence through repeated hands-on work.

How the Data Analytics Training is delivered:

Instructor-led sessions with guided demos
Hands-on labs in every module
Weekly assignments with review
Mini-projects after major topics (Excel, SQL, dashboards)
Capstone project with mentor feedback
Doubt clearing support and revision sessions
Interview-oriented practice (SQL questions, dashboard walkthroughs, case tasks)

Example of what practical means here: you might be asked to build a monthly KPI dashboard, explain why a metric dropped, and back it with SQL-based analysis.

Tools & Technologies Covered

Spreadsheets

Microsoft Excel (Pivot Tables, charts, Lookups, conditional formatting)
Google Sheets basics (where relevant)

Databases

SQL fundamentals (MySQL/PostgreSQL-style concepts)

Business Intelligence (BI)

Microsoft Power BI (data modeling basics, DAX foundations, dashboards)
Tableau (dashboard concepts, filters, charts)

Analytics Skills

Data cleaning and preparation
Exploratory Data Analysis (EDA basics)
KPI design and reporting
Dashboard storytelling and stakeholder communication

Optional (batch dependent): Python for data analytics (pandas, NumPy, Jupyter Notebook).

Certification & Industry Recognition

On completion of the Data Analytics Course, learners receive:

Course completion certificate from Ascents Learning
Project/capstone documentation for portfolio and LinkedIn
Internship/experience documentation may be available depending on the batch structure and project model

In hiring, the certificate is supportive—but your portfolio and your ability to explain your work usually matter more.

Career Opportunities After Completion

After completing this Data Analytics Course with Placement support, learners commonly apply for:

Junior Data Analyst / Associate Data Analyst
Reporting Analyst / MIS Analyst
BI Analyst (Entry Level) / Power BI Developer (Junior)
Operations Analyst
Marketing Analyst (performance reporting)
Product Analyst (metrics tracking basics)
Business Analyst (analytics-focused roles)

What companies often test:

SQL query logic (joins, filters, grouping)
Excel problem solving (pivots, lookups, cleaning)
Dashboard design and interpretation (KPIs, visuals, filters)
Communication (turning data into decisions)

Why Choose Ascents Learning

If you’re searching for the Best Data Analytics Training, here are practical factors that usually drive real outcomes.

Practical, job-aligned learning

Focus on Excel + SQL + dashboards, not just theory
Real datasets and case-style assignments

Project-first structure

Mini-projects throughout the course
A capstone project designed to be portfolio-ready

Mentorship and feedback

Doubt clearing support
Project reviews and improvement guidance (as per batch structure)

Career support (placement assistance)

If you’re considering a Data Analytics Course with Placement, Ascents Learning supports job readiness through:

Resume + LinkedIn + portfolio guidance
Mock interviews and interview question practice
Career counseling and job role mapping
Interview opportunities through partner networks as per eligibility and readiness

Flexible learning options

Weekday/weekend batches
Online/offline/hybrid options (as available)
Recorded session support (where applicable)

If you want a practical Data Analytics Course with real projects and structured career support,
connect with Ascents Learning for batch details and the course roadmap.

Call: +91-921-780-6888
Website: www.ascentslearning.com

Curriculum

61 Sections
359 Lessons
20 Weeks

Expand all sectionsCollapse all sections

Module 1: Excel Basics for Beginners
Tools Covered: Microsoft Excel (All Versions)
8
- 1.1
  Understanding Excel Interface and RibbonCopyCopyCopyCopy
- 1.2
  Working with Workbooks, Worksheets, and CellsCopyCopyCopyCopy
- 1.3
  Entering and Formatting DataCopyCopyCopyCopy
- 1.4
  Basic Excel Formulas and Functions (SUM, AVERAGE, MIN, MAX)CopyCopyCopyCopy
- 1.5
  Using AutoFill and Flash FillCopyCopyCopyCopy
- 1.6
  Basic Formatting (Fonts, Colors, Borders, Cell Styles)CopyCopyCopyCopy
- 1.7
  Adjusting Rows, Columns, and Cell SizesCopyCopyCopyCopy
- 1.8
  Basic Excel Shortcuts for EfficiencyCopyCopyCopyCopy
Module 2: Data Entry, Cleaning, and Formatting
Tools Covered: Excel Formatting, Data Cleaning Tools
6
- 2.1
  Data Validation (Drop-down Lists, Restrictions)CopyCopyCopyCopy
- 2.2
  Conditional Formatting for Data HighlightingCopyCopyCopyCopy
- 2.3
  Text Functions (LEFT, RIGHT, MID, LEN, TRIM, CONCATENATE)CopyCopyCopyCopy
- 2.4
  Handling Duplicates and Blank CellsCopyCopyCopyCopy
- 2.5
  Find and Replace, Go To SpecialCopyCopyCopyCopy
- 2.6
  Sorting and Filtering Data for Better AnalysisCopyCopyCopyCopy
Module 3: Working with Formulas and Functions
Tools Covered: Excel Formulas, Logical and Lookup Functions
5
- 3.1
  Understanding Relative, Absolute, and Mixed ReferencesCopyCopyCopyCopy
- 3.2
  Logical Functions (IF, AND, OR, IFERROR, IFS)CopyCopyCopyCopy
- 3.3
  Lookup and Reference Functions (VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH)CopyCopyCopyCopy
- 3.4
  Date and Time Functions (TODAY, NOW, DATEDIF, NETWORKDAYS)CopyCopyCopyCopy
- 3.5
  Text Manipulation with String Functions (TEXT, UPPER, LOWER, PROPER)CopyCopyCopyCopy
Module 4: Data Visualization with Charts and Graphs
Tools Covered: Excel Charts and Graphs
5
- 4.1
  Creating and Customizing Charts (Column, Bar, Line, Pie, Area)CopyCopyCopyCopy
- 4.2
  Advanced Charting (Combo Charts, Waterfall, Funnel, Histogram, Pareto)CopyCopyCopyCopy
- 4.3
  Data Labels, Titles, and Formatting TechniquesCopyCopyCopyCopy
- 4.4
  Using Sparklines for Miniature Charts in CellsCopyCopyCopyCopy
- 4.5
  Dynamic Charts using Named RangesCopyCopyCopyCopy
Module 5: Data Analysis with Pivot Tables and Pivot Charts
Tools Covered: Excel Pivot Tables and Pivot Charts
6
- 5.1
  Introduction to Pivot Tables and Pivot ChartsCopyCopyCopyCopy
- 5.2
  Creating Pivot Tables for Data AnalysisCopyCopyCopyCopy
- 5.3
  Using Slicers and Filters in Pivot TablesCopyCopyCopyCopy
- 5.4
  Summarizing Data with Pivot TablesCopyCopyCopyCopy
- 5.5
  Creating Pivot Charts for Better InsightsCopyCopyCopyCopy
- 5.6
  Grouping and Custom Calculations in Pivot TablesCopyCopyCopyCopy
Module 6: Advanced Excel Functions for Data Analysis
Tools Covered: Excel Advanced Formulas and Functions
5
- 6.1
  Advanced Lookup Functions (XLOOKUP, OFFSET, INDIRECT)CopyCopyCopyCopy
- 6.2
  Array Formulas and Dynamic Arrays (SORT, FILTER, UNIQUE)CopyCopyCopyCopy
- 6.3
  Advanced Conditional Formatting for Dynamic Data HighlightingCopyCopyCopyCopy
- 6.4
  Statistical Functions (COUNTIF, AVERAGEIF, SUMIF, RANK, PERCENTILE, QUARTILE)CopyCopyCopyCopy
- 6.5
  Data Forecasting and Trend AnalysisCopyCopyCopyCopy
Module 7: Power Query and Power Pivot for Data Modeling
Tools Covered: Power Query, Power Pivot
5
- 7.1
  Introduction to Power Query for Data Cleaning and TransformationCopyCopyCopyCopy
- 7.2
  Importing and Transforming Data using Power QueryCopyCopyCopyCopy
- 7.3
  Introduction to Power Pivot for Data ModelingCopyCopyCopyCopy
- 7.4
  Creating Relationships and Measures in Power PivotCopyCopyCopyCopy
- 7.5
  Using DAX Functions for Data Analysis in Power PivotCopyCopyCopyCopy
Module 8: Working with Large Datasets and Advanced Excel Tools
Tools Covered: Excel Data Management
5
- 8.1
  Working with Large Datasets (1M+ Rows) EfficientlyCopyCopyCopyCopy
- 8.2
  Data Consolidation from Multiple Worksheets/WorkbooksCopyCopyCopyCopy
- 8.3
  Advanced Filtering and Sorting TechniquesCopyCopyCopyCopy
- 8.4
  Using What-If Analysis (Goal Seek, Data Tables, Solver)CopyCopyCopyCopy
- 8.5
  Scenario Manager for Business ForecastingCopyCopyCopyCopy
Module 9: Excel for Business Intelligence and Reporting
Tools Covered: Power BI, Excel Dashboards
4
- 9.1
  Connecting Excel Data with Power BICopyCopyCopyCopy
- 9.2
  Creating Interactive Dashboards in ExcelCopyCopyCopyCopy
- 9.3
  Using Excel as a Data Source for Power BICopyCopyCopyCopy
- 9.4
  Exporting Data from Excel to Power BICopyCopyCopyCopy
Module 10: Projects and Interview Preparation
Tools Covered: Excel, Power Query, Power Pivot
4
- 10.1
  Real-World Data Analysis Projects using ExcelCopyCopyCopyCopy
- 10.2
  Business Case Studies (Sales Analysis, Financial Reporting, HR Data Insights)CopyCopyCopyCopy
- 10.3
  Resume Preparation for Data Analyst RolesCopyCopyCopyCopy
- 10.4
  Common Interview Questions for Data Analyst JobsCopyCopyCopyCopy
Module 11: Introduction to Databases and SQL
Tools Covered: MySQL, Microsoft SQL Server, PostgreSQL
5
- 11.1
  Understanding Databases and Relational Database Management Systems (RDBMS)CopyCopyCopyCopy
- 11.2
  Difference Between SQL and NoSQL DatabasesCopyCopyCopyCopy
- 11.3
  Database Architecture and ACID PropertiesCopyCopyCopyCopy
- 11.4
  Setting Up MySQL / SQL Server / PostgreSQL EnvironmentCopyCopyCopyCopy
- 11.5
  Introduction to SQL and Its Role in Data AnalysisCopyCopyCopyCopy
Module 12: SQL Basics – Writing Queries
Tools Covered: SQL Query Editor, MySQL Workbench, SSMS
6
- 12.1
  Introduction to SQL Syntax and Data TypesCopyCopyCopyCopy
- 12.2
  Creating and Managing DatabasesCopyCopyCopyCopy
- 12.3
  Creating, Modifying, and Deleting TablesCopyCopyCopyCopy
- 12.4
  Understanding Primary Keys, Foreign Keys, and ConstraintsCopyCopyCopyCopy
- 12.5
  Inserting, Updating, and Deleting Records (CRUD Operations)CopyCopyCopyCopy
- 12.6
  Querying Data using SELECT StatementsCopyCopyCopyCopy
Module 13: Advanced Data Retrieval and Filtering
Tools Covered: SQL Querying Tools
5
- 13.1
  Using WHERE, ORDER BY, and LIMIT ClausesCopyCopyCopyCopy
- 13.2
  Applying Logical Operators (AND, OR, NOT)CopyCopyCopyCopy
- 13.3
  Working with NULL Values and Handling Missing DataCopyCopyCopyCopy
- 13.4
  Using CASE Statements for Conditional LogicCopyCopyCopyCopy
- 13.5
  Aliasing Columns and Tables for Better ReadabilityCopyCopyCopyCopy
Module 14: Aggregations and Grouping Data
Tools Covered: SQL Functions and Queries
4
- 14.1
  Introduction to Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)CopyCopyCopyCopy
- 14.2
  Grouping Data using GROUP BYCopyCopyCopyCopy
- 14.3
  Filtering Grouped Data with HAVING ClauseCopyCopyCopyCopy
- 14.4
  Using Window Functions (ROW_NUMBER, RANK, DENSE_RANK)CopyCopyCopyCopy
Module 15: Working with Joins and Relationships
Tools Covered: SQL Joins and Relationships
5
- 15.1
  Understanding Different Types of Joins (INNER, LEFT, RIGHT, FULL)CopyCopyCopyCopy
- 15.2
  Self Joins and Cross JoinsCopyCopyCopyCopy
- 15.3
  Using UNION, UNION ALL, INTERSECT, and EXCEPTCopyCopyCopyCopy
- 15.4
  Subqueries and Nested Queries for Complex Data RetrievalCopyCopyCopyCopy
- 15.5
  Common Table Expressions (CTEs)CopyCopyCopyCopy
Module 16: Advanced SQL for Data Analysis
Tools Covered: SQL Advanced Queries
5
- 16.1
  Using String Functions (CONCAT, SUBSTRING, REPLACE, CHARINDEX)CopyCopyCopyCopy
- 16.2
  Date and Time Functions (NOW, DATEADD, DATEDIFF)CopyCopyCopyCopy
- 16.3
  Handling Complex Queries using CTEs and Recursive QueriesCopyCopyCopyCopy
- 16.4
  Pivoting Data for Business Intelligence ReportsCopyCopyCopyCopy
- 16.5
  Ranking and Analytical FunctionsCopyCopyCopyCopy
Module 17: SQL Performance Optimization and Indexing
Tools Covered: SQL Server / MySQL Performance Tools
5
- 17.1
  Understanding Indexes and Their ImportanceCopyCopyCopyCopy
- 17.2
  Optimizing Queries using IndexingCopyCopyCopyCopy
- 17.3
  Analyzing Query Execution PlansCopyCopyCopyCopy
- 17.4
  Using Stored Procedures and TriggersCopyCopyCopyCopy
- 17.5
  Transactions and Error Handling in SQLCopyCopyCopyCopy
Module 18: SQL for Data Analysis and Reporting
Tools Covered: SQL Reporting and Visualization
4
- 18.1
  Creating Views for Simplified QueryingCopyCopyCopyCopy
- 18.2
  Extracting Insights from Large DatasetsCopyCopyCopyCopy
- 18.3
  Connecting SQL Databases to Power BI for ReportingCopyCopyCopyCopy
- 18.4
  Automating SQL Queries for Business IntelligenceCopyCopyCopyCopy
Module 19: Real-World Projects and Interview Preparation
Tools Covered: SQL, Power BI, Excel
4
- 19.1
  Real-World Data Analysis Projects using SQLCopyCopyCopyCopy
- 19.2
  Business Case Studies (Sales Analysis, Customer Segmentation, Financial Reports)CopyCopyCopyCopy
- 19.3
  Resume Preparation for Data Analyst RolesCopyCopyCopyCopy
- 19.4
  Common SQL Interview Questions and Mock InterviewsCopyCopyCopyCopy
Module 20: Introduction to Python
16
- 20.1
  Overview of PythonCopyCopyCopyCopy
- 20.2
  History and Versions of PythonCopyCopyCopyCopy
- 20.3
  Features of Python:CopyCopyCopyCopy
- 20.4
  Simple and Open SourceCopyCopyCopyCopy
- 20.5
  High-Level Programming LanguageCopyCopyCopyCopy
- 20.6
  Portable and InterpretedCopyCopyCopyCopy
- 20.7
  Object-Oriented & ProceduralCopyCopyCopyCopy
- 20.8
  Easy to MaintainCopyCopyCopyCopy
- 20.9
  Comparison of Python with Other LanguagesCopyCopyCopyCopy
- 20.10
  Java vs. PythonCopyCopyCopyCopy
- 20.11
  C++ vs. PythonCopyCopyCopyCopy
- 20.12
  JavaScript vs. PythonCopyCopyCopyCopy
- 20.13
  Perl vs. PythonCopyCopyCopyCopy
- 20.14
  Executing Python ProgramsCopyCopyCopyCopy
- 20.15
  Python Interactive Mode vs. Script ModeCopyCopyCopyCopy
- 20.16
  Comments in PythonCopyCopyCopyCopy
Module 21: Python Variables & Data Types
11
- 21.1
  Understanding Variables in PythonCopyCopyCopyCopy
- 21.2
  Assigning and Declaring VariablesCopyCopyCopyCopy
- 21.3
  Data Types in PythonCopyCopyCopyCopy
- 21.4
  Numeric Data Types (int, float, complex)CopyCopyCopyCopy
- 21.5
  Boolean Data TypeCopyCopyCopyCopy
- 21.6
  Compound Data TypesCopyCopyCopyCopy
- 21.7
  ListsCopyCopyCopyCopy
- 21.8
  TuplesCopyCopyCopyCopy
- 21.9
  DictionariesCopyCopyCopyCopy
- 21.10
  SetsCopyCopyCopyCopy
- 21.11
  ArraysCopyCopyCopyCopy
Module 22: Operators in Python
10
- 22.1
  Types of OperatorsCopyCopyCopyCopy
- 22.2
  Arithmetic OperatorsCopyCopyCopyCopy
- 22.3
  Relational (Comparison) OperatorsCopyCopyCopyCopy
- 22.4
  Assignment OperatorsCopyCopyCopyCopy
- 22.5
  Logical (Boolean) OperatorsCopyCopyCopyCopy
- 22.6
  Identity OperatorsCopyCopyCopyCopy
- 22.7
  Membership OperatorsCopyCopyCopyCopy
- 22.8
  Bitwise OperatorsCopyCopyCopyCopy
- 22.9
  Operator Precedence and AssociativityCopyCopyCopyCopy
- 22.10
  Understanding Order of Execution in PythonCopyCopyCopyCopy
Module 23: Conditional Statements in Python
3
- 23.1
  Control Flow StatementsCopyCopyCopyCopy
- 23.2
  if StatementCopyCopyCopyCopy
- 23.3
  if-else StatementCopyCopyCopyCopy
Module 24: Looping in Python
7
- 24.1
  Loops in PythonCopyCopyCopyCopy
- 24.2
  for LoopCopyCopyCopyCopy
- 24.3
  while LoopCopyCopyCopyCopy
- 24.4
  Loop Control StatementsCopyCopyCopyCopy
- 24.5
  break StatementCopyCopyCopyCopy
- 24.6
  continue StatementCopyCopyCopyCopy
- 24.7
  pass StatementCopyCopyCopyCopy
Module 25 Working with Numbers in Python
3
- 25.1
  Mathematical FunctionsCopyCopyCopyCopy
- 25.2
  Random FunctionsCopyCopyCopyCopy
- 25.3
  Trigonometric FunctionsCopyCopyCopyCopy
Module 26: Working with Strings in Python
4
- 26.1
  Accessing StringsCopyCopyCopyCopy
- 26.2
  String OperationsCopyCopyCopyCopy
- 26.3
  String SlicingCopyCopyCopyCopy
- 26.4
  Built-in String FunctionsCopyCopyCopyCopy
Module 27: Working with Lists in Python
7
- 27.1
  Introduction to ListsCopyCopyCopyCopy
- 27.2
  Accessing and Modifying ListsCopyCopyCopyCopy
- 27.3
  List Slicing and OperationsCopyCopyCopyCopy
- 27.4
  Aliasing and Cloning ListsCopyCopyCopyCopy
- 27.5
  List ComprehensionsCopyCopyCopyCopy
- 27.6
  Deleting ListsCopyCopyCopyCopy
- 27.7
  Built-in List FunctionsCopyCopyCopyCopy
Module 28: Working with Tuples in Python
4
- 28.1
  Introduction to TuplesCopyCopyCopyCopy
- 28.2
  Creating, Accessing, and Modifying TuplesCopyCopyCopyCopy
- 28.3
  Deleting TuplesCopyCopyCopyCopy
- 28.4
  Built-in Tuple FunctionsCopyCopyCopyCopy
Module 29: Working with Dictionaries in Python
4
- 29.1
  Introduction to DictionariesCopyCopyCopyCopy
- 29.2
  Creating, Accessing, and Modifying DictionariesCopyCopyCopyCopy
- 29.3
  Deleting Dictionary ElementsCopyCopyCopyCopy
- 29.4
  Built-in Dictionary FunctionsCopyCopyCopyCopy
Module 30: Working with Sets in Python
4
- 30.1
  Introduction to SetsCopyCopyCopyCopy
- 30.2
  Creating and Accessing SetsCopyCopyCopyCopy
- 30.3
  Set Operations (Union, Intersection, Difference, etc.)CopyCopyCopyCopy
- 30.4
  Built-in Set FunctionsCopyCopyCopyCopy
Module 31: Date & Time Handling in Python
2
- 31.1
  Working with time ModuleCopyCopyCopyCopy
- 31.2
  Working with calendar ModuleCopyCopyCopyCopy
Module 32: Functions in Python
5
- 32.1
  Defining and Calling FunctionsCopyCopyCopyCopy
- 32.2
  Types of FunctionsCopyCopyCopyCopy
- 32.3
  Function Arguments (Positional, Keyword, Default, Variable-Length)CopyCopyCopyCopy
- 32.4
  Lambda (Anonymous) FunctionsCopyCopyCopyCopy
- 32.5
  Scope of Variables (Global & Local Variables)CopyCopyCopyCopy
Module 33: Working with Modules in Python
4
- 33.1
  Introduction to ModulesCopyCopyCopyCopy
- 33.2
  Using import StatementCopyCopyCopyCopy
- 33.3
  Using from … import StatementCopyCopyCopyCopy
- 33.4
  Using from … import * StatementCopyCopyCopyCopy
Module 34: File Handling in Python (I/O Operations)
4
- 34.1
  Opening and Closing FilesCopyCopyCopyCopy
- 34.2
  Reading and Writing FilesCopyCopyCopyCopy
- 34.3
  Renaming and Removing FilesCopyCopyCopyCopy
- 34.4
  Working with DirectoriesCopyCopyCopyCopy
Module 35: Exception Handling in Python
6
- 35.1
  Understanding Errors in PythonCopyCopyCopyCopy
- 35.2
  Syntax ErrorsCopyCopyCopyCopy
- 35.3
  ExceptionsCopyCopyCopyCopy
- 35.4
  Handling Exceptions in PythonCopyCopyCopyCopy
- 35.5
  Raising ExceptionsCopyCopyCopyCopy
- 35.6
  User-Defined ExceptionsCopyCopyCopyCopy
Module 36: Object-Oriented Programming (OOP) in Python
5
- 36.1
  Understanding Classes and ObjectsCopyCopyCopyCopy
- 36.2
  Attributes and MethodsCopyCopyCopyCopy
- 36.3
  Inheritance (Single, Multiple, Multilevel)CopyCopyCopyCopy
- 36.4
  Method Overloading and OverridingCopyCopyCopyCopy
- 36.5
  Encapsulation and AbstractionCopyCopyCopyCopy
Module 37: Introduction to Anaconda Distribution
Python Modules Curriculum Pandas, Numpy, Matplotlib and Seaborn
7
- 37.1
  🛠 Tools Covered: Anaconda, Python, Jupyter Notebook, PyCharmCopyCopyCopyCopy
- 37.2
  What is Anaconda Distribution?CopyCopyCopyCopy
- 37.3
  Difference between Anaconda and Python DistributionCopyCopyCopyCopy
- 37.4
  How to install Anaconda?CopyCopyCopyCopy
- 37.5
  Anaconda Repository: Understanding conda packages and environmentsCopyCopyCopyCopy
- 37.6
  Anaconda Navigator: Managing libraries and environmentsCopyCopyCopyCopy
- 37.7
  Integrating Anaconda with PyCharm for seamless codingCopyCopyCopyCopy
Module 38: Using Git and GitHub
🛠 Tools Covered: Git, GitHub
5
- 38.1
  Setting up GitHub AccountCopyCopyCopyCopy
- 38.2
  Configuring Your First Git RepositoryCopyCopyCopyCopy
- 38.3
  Making First Git CommitCopyCopyCopyCopy
- 38.4
  Pushing First Commit to GitHubCopyCopyCopyCopy
- 38.5
  Understanding Git and GitHub Workflow (Step-by-Step)CopyCopyCopyCopy
Module 39: Introduction to NumPy & Statistical Analysis
Tools Covered: NumPy, Python
7
- 39.1
  What is NumPy?CopyCopyCopyCopy
- 39.2
  Performance Testing of NumPy vs ListsCopyCopyCopyCopy
- 39.3
  NumPy Arrays and MatricesCopyCopyCopyCopy
- 39.4
  Indexing and Selection in NumPyCopyCopyCopyCopy
- 39.5
  NumPy Operations: Array with Array, Scalars, Universal FunctionsCopyCopyCopyCopy
- 39.6
  Working with Flat Files using NumPyCopyCopyCopyCopy
- 39.7
  Mathematical and Statistical Functions in NumPyCopyCopyCopyCopy
Module 40: Introduction to Pandas & Data Analysis
Tools Covered: Pandas, Python
8
- 40.1
  What is Pandas?CopyCopyCopyCopy
- 40.2
  Creating Pandas Series and DataFramesCopyCopyCopyCopy
- 40.3
  Grouping, Sorting, and Filtering DataCopyCopyCopyCopy
- 40.4
  Merging, Joining, and Concatenating DataFramesCopyCopyCopyCopy
- 40.5
  Handling Missing Data (Imputation Techniques)CopyCopyCopyCopy
- 40.6
  Pandas Operations for Data AnalysisCopyCopyCopyCopy
- 40.7
  Data Input and Output (CSV, Excel, JSON, Databases)CopyCopyCopyCopy
- 40.8
  Hands-on Practical Use Cases using PandasCopyCopyCopyCopy
Module 41: Statistics and Probability
Concepts Covered: Statistical Analysis, Probability Theory
9
- 41.1
  Types of Datasets: Numerical, Categorical, OrdinalCopyCopyCopyCopy
- 41.2
  Descriptive Statistics: Mean, Median, ModeCopyCopyCopyCopy
- 41.3
  Variance & Standard DeviationCopyCopyCopyCopy
- 41.4
  Probability Functions:CopyCopyCopyCopy
- 41.5
  Probability Density Function (PDF)CopyCopyCopyCopy
- 41.6
  Probability Mass Function (PMF)CopyCopyCopyCopy
- 41.7
  Percentiles and MomentsCopyCopyCopyCopy
- 41.8
  Covariance vs CorrelationCopyCopyCopyCopy
- 41.9
  Conditional Probability & Bayes’ TheoremCopyCopyCopyCopy
Module 42: Data Visualization using Matplotlib
Tools Covered: Matplotlib, Python
7
- 42.1
  Understanding Exploratory Data Analysis (EDA)CopyCopyCopyCopy
- 42.2
  Plotting Line Graphs on Time-Series DataCopyCopyCopyCopy
- 42.3
  Pie Charts, Bar Charts, and Horizontal Bar GraphsCopyCopyCopyCopy
- 42.4
  Introduction to the IRIS DatasetCopyCopyCopyCopy
- 42.5
  2D Scatter Plots & Pair PlotsCopyCopyCopyCopy
- 42.6
  Histograms & Probability Density Function (PDF)CopyCopyCopyCopy
- 42.7
  Cumulative Distribution Function (CDF)CopyCopyCopyCopy
Module 43: Data Visualization using Seaborn
Tools Covered: Seaborn, Python
5
- 43.1
  Introduction to SeabornCopyCopyCopyCopy
- 43.2
  Scatter Plots & Figure CustomizationCopyCopyCopyCopy
- 43.3
  Line Plots for Time-Series DataCopyCopyCopyCopy
- 43.4
  Advanced Seaborn VisualizationsCopyCopyCopyCopy
- 43.5
  Hands-on Data Visualization with Real DatasetsCopyCopyCopyCopy
Module 44: Project & Interview Preparation
Projects, Career Guidance & Resume Building
7
- 44.1
  Hands-on Real-World ProjectsCopyCopyCopyCopy
- 44.2
  E-commerce Data AnalysisCopyCopyCopyCopy
- 44.3
  Retail Sales ForecastingCopyCopyCopyCopy
- 44.4
  Customer Segmentation using SQL & PandasCopyCopyCopyCopy
- 44.5
  Soft Skills & PD (Personality Development) ClassesCopyCopyCopyCopy
- 44.6
  Resume Preparation & Optimization for Data Analyst RolesCopyCopyCopyCopy
- 44.7
  Common Interview Questions & Mock Interview SessionsCopyCopyCopyCopy
Module 45: Quick Start with Power BI Service
Power BI Course Curriculum (Beginner to Advanced) | Tools Covered: Power BI Service, Power BI Desktop
6
- 45.1
  Getting Power BI Tools: Installing and Setting UpCopyCopyCopyCopy
- 45.2
  Introduction to Power BI Components and TerminologyCopyCopyCopyCopy
- 45.3
  Creating a Dashboard in Minutes (Hands-on Exercise)CopyCopyCopyCopy
- 45.4
  Refreshing Data in Power BI ServiceCopyCopyCopyCopy
- 45.5
  Interacting with Dashboards and ReportsCopyCopyCopyCopy
- 45.6
  Sharing Dashboards and ReportsCopyCopyCopyCopy
Module 46: Getting and Transforming Data with Power BI Desktop
Tools Covered: Power BI Desktop, Power Query Editor
6
- 46.1
  Introduction to Power BI DesktopCopyCopyCopyCopy
- 46.2
  Getting Data: Excel vs Power BI Desktop and ServiceCopyCopyCopyCopy
- 46.3
  Understanding Data Structure for Q&ACopyCopyCopyCopy
- 46.4
  Direct Query vs Import DataCopyCopyCopyCopy
- 46.5
  Connecting to Multiple Data SourcesCopyCopyCopyCopy
- 46.6
  Data Cleaning and Transformation using Power QueryCopyCopyCopyCopy
Module 47: Data Modeling in Power BI
Tools Covered: Power BI Desktop
7
- 47.1
  Introduction to Data ModelingCopyCopyCopyCopy
- 47.2
  Setting Up and Managing RelationshipsCopyCopyCopyCopy
- 47.3
  Understanding Cardinality and Cross-FilteringCopyCopyCopyCopy
- 47.4
  Default Summarization and Sorting OptionsCopyCopyCopyCopy
- 47.5
  Creating Calculated ColumnsCopyCopyCopyCopy
- 47.6
  Creating Measures and Quick MeasuresCopyCopyCopyCopy
- 47.7
  Optimizing Data Models for PerformanceCopyCopyCopyCopy
Module 48: Data Visualization in Power BI
Tools Covered: Power BI Desktop
20
- 48.1
  Creating Different Types of VisualizationsCopyCopyCopyCopy
- 48.2
  Formatting and Customizing VisualizationsCopyCopyCopyCopy
- 48.3
  Setting Sort Order for VisualsCopyCopyCopyCopy
- 48.4
  Using Scatter and Bubble Charts with Play AxisCopyCopyCopyCopy
- 48.5
  Tooltips and Interactive ReportsCopyCopyCopyCopy
- 48.6
  Slicers, Timeline Slicers, and Sync SlicersCopyCopyCopyCopy
- 48.7
  Cross Filtering and HighlightingCopyCopyCopyCopy
- 48.8
  Applying Visual, Page, and Report Level FiltersCopyCopyCopyCopy
- 48.9
  Drill Down and Drill Up FunctionalityCopyCopyCopyCopy
- 48.10
  Working with HierarchiesCopyCopyCopyCopy
- 48.11
  Adding Reference and Constant LinesCopyCopyCopyCopy
- 48.12
  Creating Tables and Matrices with Conditional FormattingCopyCopyCopyCopy
- 48.13
  Using KPI Indicators, Cards, and GaugesCopyCopyCopyCopy
- 48.14
  Map VisualizationsCopyCopyCopyCopy
- 48.15
  Importing and Using Custom VisualsCopyCopyCopyCopy
- 48.16
  Managing and Arranging Visuals in ReportsCopyCopyCopyCopy
- 48.17
  Implementing Drill-through for In-depth AnalysisCopyCopyCopyCopy
- 48.18
  Using Custom Report ThemesCopyCopyCopyCopy
- 48.19
  Grouping and Binning DataCopyCopyCopyCopy
- 48.20
  Working with Selection Pane, Bookmarks, and ButtonsCopyCopyCopyCopy
Module 49: Power BI Service Visualization Tools
Tools Covered: Power BI Service
5
- 49.1
  Introduction to Power BI Service Visualization ToolsCopyCopyCopyCopy
- 49.2
  Creating and Managing Standalone TilesCopyCopyCopyCopy
- 49.3
  Setting Up Data-Driven AlertsCopyCopyCopyCopy
- 49.4
  Gaining Quick and Related InsightsCopyCopyCopyCopy
- 49.5
  Customizing Q&A QuestionsCopyCopyCopyCopy
Module 50: Publishing and Sharing Reports
Tools Covered: Power BI Service, Power BI Desktop
13
- 50.1
  Introduction to Power BI Publishing and Sharing OptionsCopyCopyCopyCopy
- 50.2
  Overview of Different Sharing MethodsCopyCopyCopyCopy
- 50.3
  Publishing Reports from Power BI DesktopCopyCopyCopyCopy
- 50.4
  Publishing Reports to the WebCopyCopyCopyCopy
- 50.5
  Sharing Dashboards with Power BI ServiceCopyCopyCopyCopy
- 50.6
  Creating and Managing Workspaces and Apps (Power BI Pro)CopyCopyCopyCopy
- 50.7
  Using Content Packs (Power BI Pro)CopyCopyCopyCopy
- 50.8
  Printing or Saving Reports as PDFsCopyCopyCopyCopy
- 50.9
  Implementing Row-Level Security (Power BI Pro)CopyCopyCopyCopy
- 50.10
  Exporting Data from VisualizationsCopyCopyCopyCopy
- 50.11
  Publishing Reports for Mobile ApplicationsCopyCopyCopyCopy
- 50.12
  Exporting Reports to PowerPointCopyCopyCopyCopy
- 50.13
  Summary of Sharing OptionsCopyCopyCopyCopy
Module 51: Data Refresh and Gateway Setup
Tools Covered: Power BI Service, On-Premises Gateway
5
- 51.1
  Understanding Data Refresh in Power BICopyCopyCopyCopy
- 51.2
  Configuring Automatic RefreshCopyCopyCopyCopy
- 51.3
  Setting Up and Using the Personal Gateway (Power BI Pro and 64-bit Windows)CopyCopyCopyCopy
- 51.4
  Replacing Datasets in Power BICopyCopyCopyCopy
- 51.5
  Troubleshooting Data Refresh IssuesCopyCopyCopyCopy
Module 52: Power BI and Excel Integration
Tools Covered: Power BI Desktop, Power BI Service, Excel
5
- 52.1
  Different Options for Publishing Data from Excel to Power BICopyCopyCopyCopy
- 52.2
  Pinning Excel Elements to Power BI DashboardsCopyCopyCopyCopy
- 52.3
  Connecting Excel Data using Power BI Publisher and Analyze in ExcelCopyCopyCopyCopy
- 52.4
  Publishing Excel Dashboards to Power BICopyCopyCopyCopy
- 52.5
  Uploading and Exporting Excel Data to Power BICopyCopyCopyCopy
Module 53: Power BI Projects and Interview Preparation
Tools Covered: Power BI Desktop, Power BI Service
4
- 53.1
  Comprehensive Project Work Covering All ConceptsCopyCopyCopyCopy
- 53.2
  Hands-on Real-World Scenarios and Case StudiesCopyCopyCopyCopy
- 53.3
  Resume Preparation for Power BI RolesCopyCopyCopyCopy
- 53.4
  Power BI Interview Questions and Mock Interview SessionsCopyCopyCopyCopy
Module 54: Introduction to Tableau and BI Concepts
Tableau Mastery (Optional**): Data Visualization and Business Intelligence
6
- 54.1
  Overview of Data Warehousing and Business IntelligenceCopyCopyCopyCopy
- 54.2
  Fundamentals of Data Analysis and VisualizationCopyCopyCopyCopy
- 54.3
  Business Reporting and Dashboard EssentialsCopyCopyCopyCopy
- 54.4
  Introduction to Tableau and Its ArchitectureCopyCopyCopyCopy
- 54.5
  Understanding Measures vs. DimensionsCopyCopyCopyCopy
- 54.6
  Continuous vs. Discrete Data, Value & Category AxesCopyCopyCopyCopy
Module 55: Connecting and Managing Data Sources
4
- 55.1
  Importing Data from Flat Files, Databases, and Cloud SourcesCopyCopyCopyCopy
- 55.2
  Web Services and Clipboard Data IntegrationCopyCopyCopyCopy
- 55.3
  Understanding Extracts and Incremental ExtractsCopyCopyCopyCopy
- 55.4
  Managing Tableau Server Data SourcesCopyCopyCopyCopy
Module 56: Saving and Publishing Workbooks
3
- 56.1
  Working with Packaged WorkbooksCopyCopyCopyCopy
- 56.2
  Publishing Reports to Tableau ServerCopyCopyCopyCopy
- 56.3
  Understanding Different Tableau File FormatsCopyCopyCopyCopy
Module 57: Core Data Visualization Techniques
5
- 57.1
  Creating Worksheets and DashboardsCopyCopyCopyCopy
- 57.2
  Applying Filters and Customizing Filter ActionsCopyCopyCopyCopy
- 57.3
  Understanding Row and Column ShelvesCopyCopyCopyCopy
- 57.4
  Using Marks Cards: Color, Size, Labels, Tooltips, and PathsCopyCopyCopyCopy
- 57.5
  Working with Sets, Groups, Parameters, and Calculated ColumnsCopyCopyCopyCopy
Module 58: Creating Effective Visualizations
Building Various Chart Types:
6
- 58.1
  Line, Bar, Stacked, and Dual-Axis ChartsCopyCopyCopyCopy
- 58.2
  Heat Maps, Text Tables, and Highlight TablesCopyCopyCopyCopy
- 58.3
  Symbol and Filled Maps, Pie Charts, and TreemapsCopyCopyCopyCopy
- 58.4
  Circle, Area, and Combination ChartsCopyCopyCopyCopy
- 58.5
  Scatter Plots, Histograms, and Box PlotsCopyCopyCopyCopy
- 58.6
  Gantt, Bullet, and Packed Bubble ChartsCopyCopyCopyCopy
Module 59: Advanced Features and Analytics
7
- 59.1
  Designing Interactive DashboardsCopyCopyCopyCopy
- 59.2
  Forecasting and Trend AnalysisCopyCopyCopyCopy
- 59.3
  Adding Reference Lines, Bands, and Visual HighlightsCopyCopyCopyCopy
- 59.4
  Handling Missing Values and Null DataCopyCopyCopyCopy
- 59.5
  Implementing Table Calculations and TotalsCopyCopyCopyCopy
- 59.6
  Custom Formatting, Annotations, and Layout AdjustmentsCopyCopyCopyCopy
- 59.7
  Using Dashboard Actions: Filters and HighlightsCopyCopyCopyCopy
Module 60: Tableau Server and Collaboration
4
- 60.1
  Authoring and Publishing DashboardsCopyCopyCopyCopy
- 60.2
  Managing Subscriptions and Report DistributionCopyCopyCopyCopy
- 60.3
  Embedding Dashboards into Web PagesCopyCopyCopyCopy
- 60.4
  Exporting Reports and Sharing Server ViewsCopyCopyCopyCopy
Module 61: Project Work and Interview Preparation
Note- Students can choose Power BI or Tableau. Opting for both may result in a revised fee structure. Contact us for details.
3
- 61.1
  Hands-on Data Analysis and Dashboard CreationCopyCopyCopyCopy
- 61.2
  Resume Building for Data Analyst RolesCopyCopyCopyCopy
- 61.3
  Common Tableau Interview Questions and Practical ExercisesCopyCopyCopyCopy

Requirements

Features

Target audiences

Data Analytics Course | Data Analytics Training

Course Overview

Who Should Enroll

Learning Outcomes

Work confidently with data

Use Excel for analysis and reporting

Query data using SQL

Build dashboards and reports

Communicate insights clearly

Teaching Methodology

Tools & Technologies Covered

Spreadsheets

Databases

Business Intelligence (BI)

Analytics Skills

Certification & Industry Recognition

Career Opportunities After Completion

Why Choose Ascents Learning

Practical, job-aligned learning

Project-first structure

Mentorship and feedback

Career support (placement assistance)

Flexible learning options

Curriculum

FAQ

Online Platform

Links

Contacts

Data Analytics Course

Requirements

Features

Target audiences

Data Analytics Course | Data Analytics Training

Course Overview

Who Should Enroll

Learning Outcomes

Work confidently with data

Use Excel for analysis and reporting

Query data using SQL

Build dashboards and reports

Communicate insights clearly

Teaching Methodology

Tools & Technologies Covered

Spreadsheets

Databases

Business Intelligence (BI)

Analytics Skills

Certification & Industry Recognition

Career Opportunities After Completion

Why Choose Ascents Learning

Practical, job-aligned learning

Project-first structure

Mentorship and feedback

Career support (placement assistance)

Flexible learning options

Curriculum

FAQ

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