Data Analytics Course - Hinglish

Learn to advance your career in data analytics with our specialized upskilling program. Engage in a 6-month learning program and learn advanced data analytics tools. Give your career a boost with our data analytics certification course.

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For Pro Plan

22 Apr 2025

Date of Commencement

6 Months

Duration

Recorded

Delivery Mode

Hindi + English

Language

Unlock Your Potential: Exclusive Course Offerings

Capstone Project
Practice Exercises
Live Doubt Resolution Sessions
Industry Oriented Curriculum
Certification of Completion
Q&A Forum
Assignments and Projects
Email Support

Your Guide To Upskilling: Our Curriculum

  1. Lecture 1 : Orientation
  2. Lecture 2 : LMS Overview
  1. Lecture 1 : Data Analytisc & Its Global Scope
  2. Lecture 2 : Data Analytics in Different Domains
  3. Lecture 3 : Road Map to be a Data Analyst
  4. Lecture 4 : Role Of Data Analyst?
  5. Lecture 5 : Data Analyst vs Business Analyst
  6. Lecture 6 : What is BI and Its Importance?
  7. Lecture 7 : BI Tools and Techniques
  8. Lecture 8 : Data Analysis Methods
  9. Lecture 9 : Types of Data Analysis
  10. Lecture 10 : Data Analysis Process
  1. Lecture 1 : Data Sources
  2. Lecture 2 : Primary Data
  3. Lecture 3 : Secondary Data
  4. Lecture 4 : Types of Data
  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 : 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 : 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
  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 : 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 : 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

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

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.

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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

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Frequently Asked Questions

The course spans six months from the start date.

This course will be entirely conducted online, and all resources and content will be available on pwskills.com.

Yes, you will receive a certificate upon successful completion of the course. To qualify, you must complete at least 60% of the lectures and assignments and finish at least one medium-level difficulty project on the experience portal.

We are here to support you throughout your learning journey with us. If you encounter any issues, please click on "Support" when you log in to your account at pwskills.com. Most questions are addressed in our FAQs. If you don't find an answer there, feel free to raise a ticket under the appropriate category (Academics, Technical issues, Others, etc.). You can also reach out to us via email at support@pwskills.com.

There are no major prerequisites for the course. You should be familiar with basic mathematical concepts (equivalent to 12th grade) and proficient in basic English to understand the lectures.