Data Analytics Course

Dive into the world of Data Analytics and unlock the potential of tools like Excel, SQL, Python, PowerBI, and more. Learn industry-relevant techniques to analyze and visualize data, empowering you to make data-driven decisions. Prepare to excel in a high-demand field and shape your future as a skilled Data Analyst.

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Job Assistance

For Pro Plan

19 Apr 2025

Date of Commencement

6 Months

Duration

Live + Recorded

Delivery Mode

Hindi

Language

About Data Analytics Course

Start unlocking your potential by learning cutting-edge tools, building real-world expertise, and developing critical skills needed to thrive in the dynamic field of data analytics.

Industry Professional Led Sessions

Get guidance from qualified industry professionals.

Project Portfolio

Start building a job-ready profile with a dynamic project portfolio

Career Assistance

Prepare for interviews with guidance and opportunities to showcase skills

Dedicated Peer Network

Build connections with like-minded learners to exchange ideas and experiences.

Learn Industry Skills

Fast-track your upskilling journey with industry skills and personalized guidance

Certification

Attain your certificate upon course completion to showcase your capabilities.

Unlock Your Potential: Exclusive Course Offerings

Industry-Oriented Curriculum
Comprehensive Learning Content
Weekend Live Sessions
Capstone Project
Practice Exercises
Assignments and Projects
Live Doubt Resolution Sessions
SME Support Session
Certification of Completion
Career Guidance & Interview Preparation
Email Support
Peer Networking

Your Guide To Upskilling: Our Curriculum

  1. Lecture 1 : Introduction To Python and its Importance
  2. Lecture 2 : Installation guide of Jupyter Notebook and use of Google Colab
  3. Lecture 3 : Working with python notebooks
  4. Lecture 4 : Simple Expressions, Variables
  5. Lecture 5 : Data Types/structures
  6. Lecture 6 : Lists
  7. Lecture 7 : Tuple
  8. Lecture 8 : Dictionary ,Set
  9. Lecture 9 : Branching (If Else Elif)
  10. Lecture 10 : Range
  11. Lecture 11 : Loops, Indexing
  12. Lecture 12 : Break and Continue
  1. Lecture 1 : Introduction to Functions
  2. Lecture 2 : Built_In Functions
  3. Lecture 3 : User Defined Functions
  4. Lecture 4 : Anonymous Functions (Lambda Functions)
  5. Lecture 5 : Recursive Functions
  6. Lecture 6 : Exception Handling
  7. Lecture 7 : Class& Object
  1. Lecture 1 : Introduction to Numpy
  2. Lecture 2 : Data types
  3. Lecture 3 : arange and linspace
  4. Lecture 4 : Matrix creation
  5. Lecture 5 : Random number generation
  6. Lecture 6 : Reshaping
  7. Lecture 7 : Indexing and slicing
  8. Lecture 8 : Subseting
  9. Lecture 9 : Universal Functions
  10. Lecture 10 : Broadcasting
  11. Lecture 11 : Array Math
  1. Lecture 1 : Introduction to Pandas
  2. Lecture 2 : Pandas Operations
  3. Lecture 3 : Read and write from CSV and TSV files
  4. Lecture 4 : Read and write operations from html
  5. Lecture 5 : Read and write operations from web api
  6. Lecture 6 : Creating and working with Data frames
  7. Lecture 7 : Date time manipulation
  8. Lecture 8 : Data Cleaning
  9. Lecture 9 : Data manipulation
  10. Lecture 10 : Series manipulation
  11. Lecture 11 : Data frame manipulation
  1. Lecture 1 : Introduction to Matplotlib ,Seaborn & Plotly
  2. Lecture 2 : SubPlot,ScatterPlot,Area Plot,
  3. Lecture 3 : Bar graph,Stem Plot,Step_plot,fill_Beetween Plot
  4. Lecture 4 : imshow (),pcolor mesh(),Flat shading,contour plot,contourf plot
  5. Lecture 5 : Barps Plot,Quiver,Stream Plot
  6. Lecture 6 : Histogram,Box Plot,Error Bar Plot,Violin Plot
  7. Lecture 7 : Event Plot,Hist 2D,Hex Bin Plot,Pie
  8. Lecture 8 : tricountr, tricountf
  9. Lecture 9 : Tripcolor,Tirplot
  1. Lecture 1 : Working on census data of US to analyse, factor influencing the living standard of people living USA
  1. Lecture 1 : Types of Statistics
  2. Lecture 2 : Descriptive
  3. Lecture 3 : Inferential
  4. Lecture 4 : Population and Sample
  5. Lecture 5 : Parameter and Statistics
  6. Lecture 6 : Uses of variable
  7. Lecture 7 : Dependent
  8. Lecture 8 : Independent variable
  9. Lecture 9 : Types of Variable
  10. Lecture 10 : Continuous
  11. Lecture 11 : Categorical variable
  12. Lecture 12 : Distribution types and Skewness
  13. Lecture 13 : Hypothesis testing
  14. Lecture 14 : Type 1 Error
  15. Lecture 15 : Type 2 Error
  16. Lecture 16 : Ttest (one sample and sample)
  17. Lecture 17 : ANOVA & CHi_SQUARE
  18. Lecture 18 : Covariance and Correlation
  1. Lecture 1 : Introduction to Machine Learning
  2. Lecture 2 : Supervised vs unsupervised
  3. Lecture 3 : semi supervised and reinforcement learning
  4. Lecture 4 : Supervised and unsupervised learning models
  5. Lecture 5 : Naive Bayes
  6. Lecture 6 : KNN
  7. Lecture 7 : Clustering
  1. Lecture 1 : Data preprocessing (handling missing value, outliers & Noise)
  2. Lecture 2 : Feature Selection
  3. Lecture 3 : Feature Engineering(Encoding & Scaling)
  4. Lecture 4 : EDA case study specific to Data Preparation
  1. Lecture 1 : MSE
  2. Lecture 2 : RMSE
  3. Lecture 3 : MAE
  4. Lecture 4 : Confusion Metrics
  5. Lecture 5 : Accuracy
  6. Lecture 6 : Recall
  7. Lecture 7 : Precision
  8. Lecture 8 : F1 Score
  9. Lecture 9 : Bias Variance
  10. Lecture 10 : PCA
  1. Lecture 1 : How projects are implemented
  2. Lecture 2 : Process and techniques
  1. Lecture 1 : Helping banks to identify potential clients to whom they can sell there products and services .
  1. Lecture 1 : Introduction to Data Base & Types
  2. Lecture 2 : DBMS vs RDBMS
  3. Lecture 3 : Installation
  4. Lecture 4 : MySql User Interface
  5. Lecture 5 : DDL, DML , DQL , TCL , DCL
  6. Lecture 6 : working with constraints
  7. Lecture 7 : Default , not null , check, unique, autoincrement
  8. Lecture 8 : Primary Key , Foreign key, other keys also
  9. Lecture 9 : SQL Data types - numerical , character , date , bool , other
  10. Lecture 10 : Table Import Wizard
  11. Lecture 11 : Using Query
  12. Lecture 12 : Where
  13. Lecture 13 : order by
  14. Lecture 14 : limit
  15. Lecture 15 : group by and having
  16. Lecture 16 : Arithmetic operators
  17. Lecture 17 : Comparison operators
  18. Lecture 18 : Logical operators
  19. Lecture 19 : Operators used to negate conditions
  1. Lecture 1 : Aggregate function
  2. Lecture 2 : String function
  3. Lecture 3 : Temporal functions
  4. Lecture 4 : Intersect
  5. Lecture 5 : Except
  6. Lecture 6 : Union
  7. Lecture 7 : Union all
  1. Lecture 1 : Self Join
  2. Lecture 2 : Inner Join
  3. Lecture 3 : Left Join
  4. Lecture 4 : Right Join
  5. Lecture 5 : Full Outer join
  6. Lecture 6 : Cross Join
  7. Lecture 7 : Natural Join
  1. Lecture 1 : Types Of Sub-query
  2. Lecture 2 : Using From and Where Clause
  3. Lecture 3 : Filter Query Results using Query From Different Table
  4. Lecture 4 : Case Statement
  5. Lecture 5 : CTE
  6. Lecture 6 : Recurssive CTE
  1. Lecture 1 : Partition
  2. Lecture 2 : Rank and Dense Rank
  3. Lecture 3 : Row Number
  4. Lecture 4 : Lead and Lag Functions
  5. Lecture 5 : Min, Max, Avg, Count
  6. Lecture 6 : Unbounded Preceding, Preceding, Following
  7. Lecture 7 : System Functions
  8. Lecture 8 : User Defined Functions
  9. Lecture 9 : Views
  10. Lecture 10 : Cursor
  1. Lecture 1 : Introduction to ER diagram
  2. Lecture 2 : Mapping Constraints
  3. Lecture 3 : Cardinality
  4. Lecture 4 : Relational Model Concept
  5. Lecture 5 : 1NF
  6. Lecture 6 : 2NF
  7. Lecture 7 : 3NF
  8. Lecture 8 : BCNF
  1. Lecture 1 : Working on Airport data to analyse flights data and identify factor which can optimise operations.
  1. Lecture 1 : Launching Excel
  2. Lecture 2 : Quick access toolbar, Title Bar, Ribbon
  3. Lecture 3 : Menu Bar, Formula Bar, Status Bar
  4. Lecture 4 : Scroll Bar, Column Bar, Row Bar, Leaf Bar
  5. Lecture 5 : Name box, Workspace, Dialog box launcher
  6. Lecture 6 : Zoom controls, View buttons
  7. Lecture 7 : Tabs
  8. Lecture 8 : Row number, Column Name
  9. Lecture 9 : Inserting and Deleting Columns and Rows
  10. Lecture 10 : Range of cells
  11. Lecture 11 : Inserting & Deleting Cells
  12. Lecture 12 : Inserting Multiple Columns & Rows
  13. Lecture 13 : Modifying Cell Width and Height
  14. Lecture 14 : Hiding and Unhiding Rows and Columns
  15. Lecture 15 : Freeze panes
  16. Lecture 16 : Entering and editing data
  17. Lecture 17 : Font, Color fill
  18. Lecture 18 : Alignment
  19. Lecture 19 : Cut, Copy, Paste, Paste Special, Undo, Redo
  1. Lecture 1 : Adding, Renaming, Sliding through and hiding worksheets
  2. Lecture 2 : Copying, deleting, grouping, ungrouping worksheets
  3. Lecture 3 : Moving, Copying, Deleting and Hiding Grouped Worksheets
  1. Lecture 1 : Predefined Functions
  2. Lecture 2 : Arithmetic Functions
  3. Lecture 3 : Logical Functions and error Functions
  4. Lecture 4 : Date Functions
  5. Lecture 5 : Mathematical Functions
  6. Lecture 6 : Text Functions
  7. Lecture 7 : Statistical Functions
  8. Lecture 8 : Database Functions and Array
  9. Lecture 9 : Lookup
  10. Lecture 10 : Vlook up
  11. Lecture 11 : Hlookup
  12. Lecture 12 : Xlookup
  13. Lecture 13 : Index
  14. Lecture 14 : Index and Match
  15. Lecture 15 : Offset
  1. Lecture 1 : Number Formatting
  2. Lecture 2 : Date Formatting
  3. Lecture 3 : Converting Data into Excel Table
  4. Lecture 4 : Sorting
  5. Lecture 5 : Find and Replace
  6. Lecture 6 : Handling Duplicates
  7. Lecture 7 : Filter and Advance Filter
  8. Lecture 8 : Text to columns
  9. Lecture 9 : Senario Manager
  10. Lecture 10 : Goal Seek
  11. Lecture 11 : What If Analysis
  12. Lecture 12 : Consolidate
  13. Lecture 13 : Type Check
  14. Lecture 14 : Format Check
  15. Lecture 15 : Correctness Check
  16. Lecture 16 : Consistency
  17. Lecture 17 : Uniqueness Check
  1. Lecture 1 : Conditional Formatting
  2. Lecture 2 : Charts
  3. Lecture 3 : Charts Formatting
  4. Lecture 4 : Pivot Tables
  5. Lecture 5 : Slicers
  6. Lecture 6 : Dashboard creation
  7. Lecture 7 : Formatting Dashboard
  1. Lecture 1 : What is Macro?
  2. Lecture 2 : Recording a Macro
  3. Lecture 3 : Adding a Developer Tab
  4. Lecture 4 : Macros by button
  5. Lecture 5 : Editing Macros
  6. Lecture 6 : VBA - Subs, Ranges and sheets
  7. Lecture 7 : If statements and conditions
  8. Lecture 8 : Loops
  9. Lecture 9 : Message boxes and Input Boxes
  1. Lecture 1 : Unlocking Cells
  2. Lecture 2 : Worksheet Protection
  3. Lecture 3 : Work Book Protection
  4. Lecture 4 : Password Protecting Excel Files
  1. Lecture 1 : Pricing Model From smartwatch Company
  1. Lecture 1 : What is Power BI
  2. Lecture 2 : Power BI Products
  3. Lecture 3 : Components of Power BI
  4. Lecture 4 : Why Power BI - PowerBI Vs Other BI Tools
  5. Lecture 5 : Building Blocks of Power BI
  6. Lecture 6 : Power BI Process
  7. Lecture 7 : Power BI Desktop Installation
  8. Lecture 8 : User Interface
  9. Lecture 9 : Connecting to a local data Sources
  10. Lecture 10 : Power Query User Interface
  11. Lecture 11 : data types
  12. Lecture 12 : Home Tab
  13. Lecture 13 : Transform Tab
  14. Lecture 14 : Add Column
  15. Lecture 15 : Other Transformations
  1. Lecture 1 : Bar/Column charts
  2. Lecture 2 : Pie & Donut charts
  3. Lecture 3 : Waterfall chart
  4. Lecture 4 : Tree Map
  5. Lecture 5 : Table & Matrix visualization
  6. Lecture 6 : Ribbon chart
  7. Lecture 7 : Funnel
  8. Lecture 8 : Line
  9. Lecture 9 : Slicers
  10. Lecture 10 : KPI
  11. Lecture 11 : Area
  12. Lecture 12 : Cards
  13. Lecture 13 : Scatter
  14. Lecture 14 : Gauge Chart
  15. Lecture 15 : Maps
  16. Lecture 16 : Custom Visuals (Sankey, Sunburst, Playaxis, Wordcloud, Butterfly)
  1. Lecture 1 : Data models, Importance
  2. Lecture 2 : Schema
  3. Lecture 3 : Creating and Managing relationships
  4. Lecture 4 : Optimizing Data Models
  1. Lecture 1 : DAX and its Importance
  2. Lecture 2 : Row & Filter context
  3. Lecture 3 : Measures & Calculated columns
  4. Lecture 4 : SUM function
  5. Lecture 5 : SUMX function
  6. Lecture 6 : MIN function
  7. Lecture 7 : MINX function
  8. Lecture 8 : MAX function
  9. Lecture 9 : MAXX function
  10. Lecture 10 : AVERAGE function
  11. Lecture 11 : AVERAGEX function
  12. Lecture 12 : COUNT function
  13. Lecture 13 : DISTINCTCOUNT function
  14. Lecture 14 : ALLSELECT function
  15. Lecture 15 : FILTER function
  16. Lecture 16 : CALCULATE function
  17. Lecture 17 : CROSSFILTER function
  18. Lecture 18 : RELATED function
  19. Lecture 19 : CALENDAR function
  20. Lecture 20 : DATE function
  21. Lecture 21 : DATEDIFF function
  22. Lecture 22 : MONTH function
  23. Lecture 23 : WEEKDAY function
  24. Lecture 24 : YEAR function
  25. Lecture 25 : DATEADD function
  26. Lecture 26 : DATESBETWEEN function
  27. Lecture 27 : TOTALMTD function
  28. Lecture 28 : TOTALQTD function
  29. Lecture 29 : TOTALYTD function
  30. Lecture 30 : CONCATENATE function
  31. Lecture 31 : CONCATENATEX function
  32. Lecture 32 : FORMAT function
  33. Lecture 33 : LEFT function
  34. Lecture 34 : RIGHT function
  35. Lecture 35 : TRIM function
  36. Lecture 36 : FIND function
  37. Lecture 37 : SEARCH function
  38. Lecture 38 : AND function
  39. Lecture 39 : OR function
  40. Lecture 40 : NOT function
  41. Lecture 41 : FALSE function
  42. Lecture 42 : TRUE function
  1. Lecture 1 : Introduction to using Excel data in Power BI
  2. Lecture 2 : Exploring live connections to data with Power BI
  3. Lecture 3 : Connecting directly to Database
  4. Lecture 4 : Import Power View and Power Pivot to Power BI
  5. Lecture 5 : Power BI Publisher for Excel
  6. Lecture 6 : Content packs
  7. Lecture 7 : Introducing Power BI Mobile
  1. Lecture 1 : What is a report ?
  2. Lecture 2 : Filters ; Visual level filter
  3. Lecture 3 : Filters ; Page level filter
  4. Lecture 4 : Filters ; Report level filter
  5. Lecture 5 : Filters ; Drill through filter
  6. Lecture 6 : Include, Exclude Filter
  7. Lecture 7 : Formating a report
  1. Lecture 1 : Creating an account on PBI service
  2. Lecture 2 : Introduction to PBI Service
  3. Lecture 3 : Publishing a report to PBI Service
  4. Lecture 4 : Creating a dashboard
  5. Lecture 5 : Creating a gateway
  6. Lecture 6 : Scheduling a refresh
  7. Lecture 7 : Row level security
  8. Lecture 8 : Introduction
  9. Lecture 9 : M Language Functions
  1. Lecture 1 : Working on superstore dataset to analyse and create insightful dashboard which can help business grow.
  1. Lecture 1 : The Kinesis Family
  2. Lecture 2 : Amazon Kinesis Data Streams
  3. Lecture 3 : Amazon Kinesis Data Firehose
  4. Lecture 4 : Data Transfer with Snowball and Direct Connect
  5. Lecture 5 : AWS Database Migration Service
  1. Lecture 1 : Data Storage with Amazon S3
  2. Lecture 2 : Other AWS Data Storage Solutions
  1. Lecture 1 : Processing Data with Lambda and Glue
  2. Lecture 2 : Understanding the Hadoop Ecosystem
  3. Lecture 3 : Processing Data with EMR
  4. Lecture 4 : Automating Data Processing
  1. Lecture 1 : Data Analytics on AWS - Amazon Elasticsearch
  2. Lecture 2 : Amazon Athena
  3. Lecture 3 : Kinesis Data Analytics
  4. Lecture 4 : Amazon Redshift
  1. Lecture 1 : Introduction to AWS QuickSight
  2. Lecture 2 : AWS QuickSight in Action
  1. Lecture 1 : AWS Authorization and Authentication Mechanism
  2. Lecture 2 : AWS Data Protection and Encryption Strategies

Real-World Projects: Apply What You Learn

Python Project

Working on census data of US to analyze the factors that influence the living standards of people in the USA

SQL Project

Working on airport data to analyse flights data and identify factors which can optimize operations.

Guidance By Professionals: Our Esteemed Faculties

Experience excellence in mentorship from industry-leading professional.

Ekta Negi

Fractal | Deloitte | Capgemini

  • 5+ Years

    Work Experience

  • 3+ Years

    Teaching Experience

Data Scientist - Fractal | Capgemini, Deloitte | Business Insights | ML & Analysis Specialist

Ajay Kumar Gupta

Synopsis

  • 4+ Years

    Work Experience

  • 3+ Years

    Teaching Experience

Senior Data Scientist, Synopsys, Ex-Novartis, 4 years of experience

Saurabh Moharikar

PhysicsWallah

  • 8+ Years

    Work Experience

  • 5+ Years

    Teaching Experience

With 13 years of expertise in Data Analytics , Saurabh is passionate towards teaching.

Sonam Soni

#

  • 12+ Years

    Work Experience

  • 12+ Years

    Teaching Experience

With over 12 years of dedicated training and development in the IT industry, I specialize in providing professional enrichment to individuals and corporate teams.

Uttam

EY

  • 13+ Years

    Work Experience

  • 6+ Years

    Teaching Experience

Uttam Grade is a seasoned Data Scientist with over five years of experience driving innovation and delivering impactful solutions across various industries. Currently serving as a Senior Data Scientist at EY GDS, Uttam leverages his expertise in Python, SQL, Power BI, machine learning, NLP, and deep learning to solve complex business challenges.

Still Confused? Let us clear all your queries

Validating Your Success: About Your Certificate

You will be able to generate the certificate for course of completion:

  • After watching 60% of videos
  • After completing 60% in Quiz & Assignment
  • The above criteria is only for getting the course completion certificate. For details regarding Job Assistance criteria, please refer to the FAQs.

Talk to Our Counsellor

Get Expert Advice our Counsellor will reach within 24 hour

Champions of Change: Alumni Experiences

Unlock the potential within our alumni's experiences and witness the transformative power of upscaling on their careers and lives

"PW Skills honed my technical prowess for Data Analyst at Petpooja and nurtured crucial soft skills. Faculty guidance & holistic curriculum were invaluable for corporate readiness." .

Parimal Parmar

Data Analyst

The guidance and expertise of PW Skills' faculty were invaluable in my journey to becoming an Analytics Consultant at Inventics Software Pvt. Ltd. Their comprehensive course curriculum equipped me with the skills needed to excel in the industry. .

Varun

Analytics Consultant

Data Analytics Course

PW Skills provides a structured learning platform for individuals looking to build technical expertise in data analytics. Our Data Analytics Online Course offers a comprehensive curriculum, preparing learners for real-world career opportunities in just six months. The program is designed to deliver affordable, hands-on, and interactive training, ensuring learners gain industry-relevant skills in data collection, analysis, and visualization.

According to global market trends, data analytics is emerging as a key career field, with a significant increase in demand across industries. The ability to analyze and interpret large volumes of data is a crucial skill, making data analysts highly valuable professionals in today’s job market.

By enrolling in the Data Analytics Course with Certification, learners will gain expertise in SQL, Python, and data visualization tools to extract actionable insights and make data-driven decisions.

Why Choose PW Skills for Data Analytics?

PW Skills offers quality training at an affordable price, helping learners develop foundational and advanced analytics skills. Our curriculum is designed by industry professionals, ensuring that learners are prepared for the competitive job market.

Types of Data Analytics Course On PW Skills

PW Skills is now offering course modules especially dedicated to individuals based on their knowledge and expertise in the data analytics field. Choose the plan which is better inclined with your goals in your career.

Basic Course 

This is a beginners program type where candidates can get outcome driven results from short duration courses for data analysis. Get everything you need to start your career in data analysis. This beginner friendly course has the following benefits as mentioned below.

Name of the Course PW Skills Data Analytics Course (Basic)
Delivery Mode Pre-Recorded Lectures
Duration  6 Months
Type/Positioning Job Readiness Course
Certification Yes
Support(Email/Dashboard doubt support/ community Channel) Yes
Live Doubt Session Every Week (Once)
Industry Based Projects Yes
Job Readiness  Yes
Certification Criteria 
  • More than 60% of score in quiz
  • More than 60% video completion

Pro Version 

The pro version provides you with complete live and recorded tutorials within the course curriculum to help you better prepare and interact throughout the course.

Name of the Course PW Skills Data Analytics Course(Pro)
Delivery Mode Pre-Recorded Lectures + Live 
Duration 6 Months
Type/Positioning Job Readiness Course + Live Sessions (Weekly 1 Live)
Certification Yes
Support(Email/Dashboard doubt support/ community Channel) Yes
Live Doubt Session Every Week (Once)
Industry Based Projects Yes
Job Readiness  Yes
Assignment with Written and Video Solutions Yes
Resume Building Support  Yes (Pre-Recorded)
Ace Interview Live Training  Yes
Ace Interview Practice Sessions Yes
Job assistance  Yes
Certification Criteria 
  • More than 60% of score in quiz
  • More than 60% video completion
  • Internship Completion

Pro+ Version 

This advanced course program offers you the complete preparation beginning from scratch to mastering all major concepts and frameworks of data analysis. This course can be of duration 6 or more months.

Name of the Course PW Skills Data Analytics Course (Pro+)
Delivery Mode Pre-Recorded Lectures + Live Sessions 
Duration 6 Months
Type/Positioning Job Readiness Course + Live Sessions (Weekly 1 Live)
Certification Yes
Daily Live Doubt Sessions Daily
1-1 Expert Doubt Support  Yes 
Support(Email/Dashboard doubt support/ community Channel) Yes
Live Doubt Session Every Week (Once)
Industry Based Projects Yes
Job Readiness  Yes
Industry Mentor Webinar Yes
Assignment with Written and Video Solutions Yes
Resume Building Support  Yes
Ace Interview Live Training  Yes
Ace Interview Practice Sessions Yes
Job assistance  Yes
Certification Criteria 
  • More than 60% of score in quiz and Assignments
  • More than 60% video completion
  • Internship Completion

Key Benefits of the Data Analytics Course

✔ Career-Focused Curriculum: Learn essential skills required for data-driven job roles.
✔ Interactive Learning Resources: Access revision sessions, Q&A forums, and career guidance workshops.
✔ Certification: Earn a PW Skills certification after successfully completing course modules and assessments.
✔ Hands-On Training: Work on real-world projects through our experience portal, featuring internship projects.

At PW Skills, we integrate cutting-edge technologies into our learning modules to provide up-to-date insights on data analytics tools and applications.

Key Features of the Data Analytics Course

✔ Comprehensive Training in Data Analytics & Visualization.
✔ Exposure to Data Analytics Technologies, including Python, SQL, Power BI, and Machine Learning.
✔ Blended Learning Mode with Live & Recorded Sessions.
✔ Certification & Placement Assistance upon course completion.
✔ Guidance from Industry Professionals with years of experience in data analytics and machine learning.

Learners can choose between self-paced and live courses, catering to beginners and professionals. The curriculum covers key analytics tools and technologies to help learners build problem-solving skills and optimize business decisions.

Certification in Data Analytics & Industry-Relevant Curriculum

Earn a recognized certification after successfully completing the Data Analytics Course. The certificate validates expertise in data analysis techniques, ensuring learners are job-ready.

Benefits of Enrolling in a Data Analytics Certification Course

✔ Competitive Advantage in the job market.
✔ Career Opportunities in the data analytics field.
✔ Skills Validation to enhance credibility.
✔ Hands-On Learning Experience with real-world case studies.
✔ Professional Growth through guided mentorship.

The course includes interactive quizzes, assignments, practice questions, and hands-on projects, ensuring a strong grasp of data analytics concepts.

About PW Skills Data Analytics Course

The Data Analytics Course by PW Skills covers data collection, analysis, and visualization techniques, helping learners develop practical skills in statistical analysis, machine learning, and big data applications.

What You Will Gain from This Course

✔ Network with industry professionals for career guidance.
✔ Build a strong portfolio with real-world projects.
✔ Access job opportunities in top tech and analytics companies.
✔ Join an alumni network of skilled professionals.
✔ Earn a certification upon completion.

📌 Enroll in our Data Analytics Online Course today!

Course Highlights

1. One-on-One Doubt Support

Receive dedicated guidance from industry mentors for seamless learning.

2. Real-World Analytics Projects

Work on industry-specific case studies to gain practical exposure.

3. Hands-On Learning through Experience Portal

Showcase your skills and apply analytics concepts in real-world scenarios.

4. Practical Exercises & Assessments

Develop a strong foundation with interactive practice exercises.

5. Profile Building & Resume Sessions

Enhance your professional portfolio and LinkedIn profile for job opportunities.

6. Placement Assistance

Get support in resume preparation, interview guidance, and job applications.

Data Analytics Training Syllabus 

Check our Career-centric Data analytics classes syllabus in the table below.

Data Analytics Course Syllabus
Category Topics
Python
  • Python Basics
  • Basics Data Structure & OOPs concept
  • Advanced Python (handling errors, exceptions, logging, module creation)
Databases and Web API
  • MySQL(SQL) & MongoDB(No-SQL)
  • Creating API connections using Python
  • Basic understanding of Web application architecture
Statistics
  • Basics and Advanced Statistics
  • Statistical Concepts in Data Science
  • Feature Engineering
  • Exploratory Data Analysis
Machine Learning
  • Regression Models
  • Support Vector Machines (SVM)
  • Clustering Algorithms
  • Decision Trees and Ensemble Techniques
  • Bagging and Boosting Techniques
  • Time Series Data Analysis
Project
  • Building ML models and deploying on the cloud (Spam detection, Climate Visibility)
SQL
  • Basics of SQL
  • Loading data and creating tables
  • Keys (primary key, foreign key)
  • Functions, joins, and subqueries
  • Advanced SQL (Windows Functions, CTE Query, Data modelling)
Excel & Power BI
  • Excel Basics and Functions
  • Data Cleaning, Validation & Visualization
  • Introduction to Power BI 
  • Visualization in Power BI
  • DAX Functions
  • Advanced Power BI (Connectivity, Report creation, Architecture)
AWS
  • Collecting, Storing, Processing, Analyzing, and Visualizing Data on AWS
  • Securing Data Analytics Pipelines on AWS
Project Management
  • Lifecycle of a project
  • Project management frameworks (e.g., CRISP-DM, Agle, Scrum, etc )

Data Analyst Course Near Me 

Get certified with our data analyst course near me in India with a hands-on Certification Course by PW Skills. Anyone who wants to become an aspiring data analyst can enroll in this course.

  • Get relevant projects.
  • Learn and integrate cutting-edge technologies such as Machine Learning, Python, NumPy, Matplotlib, etc.
  • Get weekly revision sessions to revise weekly topics while learning.

With the most up-to-date curriculum and knowledgeable instructors. Get access to every study guide, sample question, test, and assessment in our course for data analytics certification.

Data Analytics Course Fees & Enrolment

Check the complete Data Analytics Course Fees for all course modules available to students. This way you can select the most suitable course based on your needs and knowledge.

At PW Skills, our main aim is to develop upskilling and affordable courses to help students from different backgrounds easily enroll and learn from the best mentors, resources, and study materials. Students can also avail EMI benefits with the course. Check the fee of all types of Data Analytics Courses below.

Data Analytics Course Fees
Name of the course Duration Course fee 
Data Analytics Course (Basic) 6 Months INR 7,000
Data Analytics Course (Pro) 6 Months INR 15,000
Data Analytics Course (Pro+) 6 Months INR 20,000

✔ Flexible payment options available.
✔ EMI and instalment plans for learner convenience.

📌 For queries, contact support@pwskills.com.

🔗 Follow us on Instagram & Telegram for updates on new courses and discounts.

How to Become a Data Analyst?

To become a data analyst, learners must learn various tools and skills. Below is the structured learning path:

1️⃣ SQL (Structured Query Language)
2️⃣ Excel & Power BI
3️⃣ Python Programming
4️⃣ Machine Learning Fundamentals
5️⃣ Data Visualization & Analytics
6️⃣ Cloud Computing with AWS
7️⃣ Project Management & Agile Frameworks
8️⃣ Capstone Projects & Industry Applications

📌 Learn essential data analysis techniques with our structured training modules.

Data Analytics Certification Criteria

To earn the Data Analytics Certification, learners must:
✔ Complete at least 70% of video lessons.
✔ Score 70% or above on quizzes and assignments.
✔ Successfully complete at least one industry project.

📌 Showcase your expertise with an certification!

Learn Data Analytics with Hands-On Projects

Our Data Analytics Course includes real-world projects, helping learners gain practical experience.

✔ Analyze census data to identify social patterns.
✔ Optimize flight operations using machine learning models.
✔ Create interactive dashboards for business intelligence applications.
✔ Build a data analytics pipeline with AWS cloud solutions.

💡 Upskill in Data Analytics and start your journey toward a high-paying career!
📌 Enroll today in the Data Analytics Online Course by PW Skills. 🚀

Still Confused?

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Frequently Asked Questions

The course lasts 6 months, with a recommended commitment of 8-10 hours per week.

Learners should have basic familiarity with mathematics and logical thinking. Some experience in programming or databases is beneficial but not mandatory, as foundational concepts are covered.

Yes, the course is designed for beginners and gradually introduces advanced concepts.

You’ll learn Python, SQL, Power BI, Tableau, AWS Services, Pandas, Excel, Data Cleaning, Data Visualization, Statistical Analysis, Dashboard Creation, Data Modeling, Machine Learning Basics, ETL Processes, and Cloud-Based Data Management.

The course lasts 6 months, with a recommended commitment of 8-10 hours per week.