Technologies You Will Learn
Python
NumPy
Pandas
Matplotlib
Seaborn
Jupyter
SQL
Excel
Power BI
Curriculum
Python with Data Analytics Curriculum
01
Introduction to Python and Data Analytics
12 Topics
Month 1
Introduction to Python
Features, applications and advantages of Python
Introduction to Data Analytics
Role of a Data Analyst in an organization
Types of Data Analytics: Descriptive, Diagnostic, Predictive and Prescriptive
Data Analyst vs Data Scientist vs Data Engineer
Data Analytics Lifecycle
Structured, Semi-Structured and Unstructured Data
Introduction to Datasets, Rows, Columns and Records
Installing Python, VS Code and Jupyter Notebook
Python Interpreter, Interactive Shell and Script Execution
Writing and Executing the First Python Program
02
Python Variables, Data Types and Operators
14 Topics
Month 1
Variables and Naming Conventions
Keywords, Identifiers and Literals
Numeric Data Types: int, float, complex
Boolean and String Data Types
The None Value
Dynamic Typing and Type Checking
Type Conversion and Type Casting
Input and Output Operations
Arithmetic Operators
Comparison and Logical Operators
Assignment and Bitwise Operators
Membership and Identity Operators
Operator Precedence
Mutable and Immutable Objects — Introduction
03
Conditional Statements and Loops
13 Topics
Month 1
if, if-else and if-elif-else
Nested Conditional Statements
Conditional Expressions
match-case
for and while Loops
The range() Function
Nested Loops
break, continue and pass
Loop else Clause
Number-Based Logical Programs
Prime Number, Factorial and Palindrome Programs
Pattern-Based Programming
Data Filtering Using Conditions and Loops
04
Python Strings and Data Processing
12 Topics
Month 1
Creating and Accessing Strings
Positive and Negative Indexing
String Slicing
String Immutability
String Formatting Using f-Strings
Common String Methods
split(), join(), replace() and strip()
Searching and Counting Text
String Validation Methods
Working with Dates and Text Representations
Removing Extra Spaces and Standardizing Text
String-Based Logical Problems
05
Python Collections
14 Topics
Month 1
Lists and List Operations
List Indexing, Slicing and Mutability
List Methods: append(), extend(), insert(), pop()
Sorting, Searching and Copying Lists
List Comprehensions
Tuples and Tuple Unpacking
Sets and Unique Values
Union, Intersection and Difference
Dictionaries and Key-Value Pairs
Dictionary Methods and Iteration
Nested Collections
Counting Frequency Using Dictionaries
Choosing the Right Data Structure
Practical Collection-Based Problems
06
Functions, Modules and Exception Handling
15 Topics
Month 1
Defining and Calling Functions
Parameters, Arguments and Return Values
Positional, Keyword and Default Arguments
*args and **kwargs
Local and Global Variables
Lambda Functions
Recursive Functions — Introduction
Built-in and User-Defined Modules
Importing Modules and Creating Packages
try, except, else and finally
Raising Exceptions Using raise
User-Defined Exceptions
File Handling: Reading and Writing Files
CSV and JSON File Introduction
Writing Reusable and Modular Programs
07
Jupyter Notebook and Data Analysis Environment
10 Topics
Month 2
Introduction to Jupyter Notebook
Creating and Managing Notebooks
Code Cells, Markdown Cells and Execution Order
Installing Libraries Using pip
Python Virtual Environments
Importing Libraries and Aliases
Introduction to Data Files: CSV, Excel, JSON and TXT
Dataset Downloading and Organization
Notebook Documentation and Code Comments
Introduction to Kaggle Datasets and Public Data Sources
08
NumPy — Numerical Computing
21 Topics
Month 2
Introduction to NumPy
NumPy Installation and Import
Creating Arrays Using array(), zeros(), ones() and arange()
Array Dimensions and the ndim Attribute
Shape, Size and Data Types
One-Dimensional and Multi-Dimensional Arrays
Indexing and Slicing Arrays
Reshaping Arrays Using reshape()
Flattening and Transposing Arrays
Array Data Types and Type Conversion
Vectorized Operations
Arithmetic and Comparison Operations
Broadcasting
Boolean Indexing and Filtering
Fancy Indexing
Aggregation: sum(), mean(), min() and max()
Standard Deviation, Variance and Percentiles
Sorting and Searching Arrays
Random Number Generation
NumPy vs Python Lists
Practical Numerical Analysis Programs
09
Pandas — Series and DataFrames
20 Topics
Month 2
Introduction to Pandas
Creating Series and DataFrames
Understanding Rows, Columns and Indexes
Importing CSV Files Using read_csv()
Reading Excel, JSON and Other Supported Formats
Exporting Data Using to_csv() and to_excel()
Exploring Data Using head(), tail() and sample()
Understanding shape, columns, dtypes and info()
Statistical Summary Using describe()
Selecting Columns and Rows
Label-Based Selection Using loc
Position-Based Selection Using iloc
Filtering Data Using Conditions
Sorting DataFrames
Adding, Updating and Removing Columns
Renaming Columns and Resetting Indexes
Applying Functions to Data
DataFrame Data Types and Type Conversion
Handling Duplicate Records
Pandas Practice Exercises
10
Data Cleaning and Transformation
16 Topics
Month 2
Introduction to Data Quality
Identifying Missing Values
Handling Missing Values Using isna(), dropna() and fillna()
Identifying and Removing Duplicate Records
Correcting Incorrect Data Types
Standardizing Column Names
Cleaning Text Using String Methods
Handling Inconsistent Categories
Detecting Invalid and Out-of-Range Values
Converting Dates and Times
Detecting and Handling Outliers — Introduction
Data Transformation and Feature Creation
Replacing Values and Mapping Categories
Applying Custom Functions
Data Validation and Quality Checks
Documenting Data Cleaning Decisions
11
Data Aggregation and Combining Datasets
14 Topics
Month 2
Sorting and Ranking Data
Grouping Data Using groupby()
Aggregation Using sum(), mean(), count() and agg()
Multiple Aggregations on Multiple Columns
Filtering Groups Using filter()
Combining DataFrames Using concat()
Joining DataFrames Using merge()
Inner, Left, Right and Outer Joins
Handling Missing Matches During Joins
Pivot Tables Using pivot_table()
Reshaping Data Using melt() and pivot()
Cross-Tabulation Using crosstab()
Working with Multiple Related Datasets
Practical Business Aggregation Exercises
12
Exploratory Data Analysis (EDA)
18 Topics
Month 3
Introduction to Exploratory Data Analysis
Objectives and Workflow of EDA
Understanding Dataset Structure and Business Context
Univariate, Bivariate and Multivariate Analysis
Numerical and Categorical Variables
Descriptive Statistics: Mean, Median, Mode and Range
Variance and Standard Deviation
Quartiles, Percentiles and Interquartile Range (IQR)
Frequency Distribution and Value Counts
Distribution Analysis and Skewness
Outlier Detection Using IQR and Z-Scores
Correlation and Covariance
Correlation Matrix and Interpretation
Cross-Tabulation and Group Comparisons
Trend and Pattern Identification
Identifying Data Quality Issues During EDA
Formulating Business Questions
Writing EDA Findings and Conclusions
13
Data Visualization Using Matplotlib
16 Topics
Month 3
Introduction to Data Visualization
Matplotlib Installation and Setup
Figure, Axes and Plot Components
Line Charts
Bar Charts and Horizontal Bar Charts
Histograms
Scatter Plots
Pie Charts and Donut Charts
Box Plots
Subplots and Multiple Charts
Titles, Labels, Legends and Annotations
Axis Formatting and Tick Labels
Figure Size, Layout and Readability
Saving Charts as Image Files
Visualizing Trends Over Time
Selecting Appropriate Charts for Different Questions
14
Statistical Visualization Using Seaborn
12 Topics
Month 3
Introduction to Seaborn
Seaborn Integration with Pandas
Count Plots and Bar Plots
Histograms and Distribution Plots
Box Plots and Violin Plots
Scatter Plots and Regression Plots
Pair Plots
Heatmaps and Correlation Matrices
Category-Based Comparisons
Styling and Figure Customization
Visualizing Outliers and Data Distributions
Creating Multi-Chart Analytical Reports
15
Business Analytics and KPI Development
13 Topics
Month 3
Introduction to Business Metrics and KPIs
Revenue, Sales Volume and Profit
Gross Profit and Profit Margin
Average Order Value (AOV)
Month-over-Month and Year-over-Year Growth
Product and Category Performance
Region-Wise and Customer-Wise Analysis
Top-N and Bottom-N Analysis
Customer Purchase Frequency
Retention and Churn — Introduction
Target vs Actual Performance
Business Questions, Findings and Recommendations
Creating an Analytical Report from EDA Results
16
Database Fundamentals and SQL
26 Topics
Month 4
Introduction to Databases and RDBMS
Tables, Rows, Columns and Data Types
Primary Keys and Foreign Keys
Database Relationships
Installing and Configuring MySQL or PostgreSQL
Creating Databases and Tables
Inserting, Updating and Deleting Records
Retrieving Data Using SELECT
Filtering Using WHERE
Sorting Using ORDER BY
DISTINCT, LIMIT and Aliases
Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
Grouping Using GROUP BY
Filtering Aggregated Results Using HAVING
SQL Operators and NULL Handling
String, Numeric and Date Functions
Conditional Expressions Using CASE
INNER JOIN and LEFT JOIN
RIGHT JOIN and FULL OUTER JOIN Concepts
Self Joins and Multiple-Table Joins
Subqueries and Common Table Expressions (CTEs)
Views and Their Use in Reporting
Window Functions: ROW_NUMBER, RANK, LAG, LEAD
Running Totals and Moving Aggregates
Data Modification and Transaction Basics
SQL Practice Questions for Data Analysts
17
Connecting Python with SQL Databases
14 Topics
Month 4
Introduction to Database Connectivity
Connecting Python to MySQL or PostgreSQL
Database Drivers and Connection Objects
Executing SQL Queries from Python
Retrieving Query Results
Parameterized Queries
Inserting and Updating Data from Python
Transactions, Commit and Rollback
Loading SQL Query Results into Pandas
Writing DataFrames into Database Tables
Querying Large Datasets Efficiently
Handling Database Connection Errors
SQL and Pandas: When to Use Each
Building a Reusable Data Extraction Script
18
Excel for Data Analysis
14 Topics
Month 4
Excel Interface and Workbook Fundamentals
Rows, Columns, Cells and Data Types
Sorting and Filtering Data
Removing Duplicates and Handling Missing Values
Text, Date and Numeric Functions
Logical Functions: IF, AND, OR, IFERROR
Lookup Functions: VLOOKUP, XLOOKUP and INDEX-MATCH
Conditional Formatting
Tables and Structured References
Pivot Tables and Pivot Charts
Slicers and Interactive Filtering
Basic KPI Reports in Excel
Importing and Exporting Excel Files Using Pandas
Cross-Checking Python Results Against Excel
19
Statistics for Data Analytics
16 Topics
Month 4
Population and Sample
Types of Data and Measurement Scales
Mean, Median and Mode
Range, Variance and Standard Deviation
Quartiles and Percentiles
Probability Fundamentals
Common Probability Distributions — Introduction
Normal Distribution and Standard Scores
Sampling and Sampling Bias
Correlation vs Causation
Introduction to Hypothesis Testing
Null and Alternative Hypotheses
p-Values and Statistical Significance
Confidence Intervals — Introduction
Comparing Groups and Interpreting Results
Statistical Interpretation in Business Scenarios
20
Power BI Fundamentals
15 Topics
Month 5
Introduction to Business Intelligence
Data Analytics vs Business Intelligence
Installing and Exploring Power BI Desktop
Power BI Interface and Report Components
Connecting to Excel, CSV and SQL Sources
Import Mode vs DirectQuery — Introduction
Introduction to Power Query
Data Profiling and Data Quality
Changing Data Types and Column Names
Removing Duplicates and Handling Missing Values
Filtering, Splitting and Merging Columns
Appending and Merging Queries
Pivoting and Unpivoting Data
Applying and Managing Query Steps
Loading Transformed Data into Power BI
21
Data Modeling in Power BI
10 Topics
Month 5
Introduction to Data Modeling
Tables, Relationships and Cardinality
One-to-Many and Many-to-Many Relationships
Primary Keys and Relationship Design
Fact Tables and Dimension Tables
Star Schema — Introduction and Design
Date Tables and Calendar-Based Analysis
Active and Inactive Relationships
Filter Direction and Relationship Behavior
Data Model Validation and Performance Basics
22
DAX and KPI Calculations
15 Topics
Month 5
Introduction to Data Analysis Expressions (DAX)
Calculated Columns vs Measures
Basic DAX Syntax and Operators
Aggregation Functions: SUM, AVERAGE, COUNT, DISTINCTCOUNT
Logical Functions: IF, SWITCH, AND, OR
Filter Functions: CALCULATE and FILTER
Row Context and Filter Context
Related Data Using RELATED
Percentage and Ratio Calculations
Profit, Margin and Average Order Value Measures
Date Intelligence — Introduction
Month-to-Date and Year-to-Date Calculations
Previous-Period Comparisons
Year-over-Year Growth
KPI Validation and Measure Testing
23
Power BI Visualization and Dashboard Development
15 Topics
Month 5
Choosing the Right Visual for a Business Question
Cards and KPI Indicators
Bar, Column and Line Charts
Combo Charts and Trend Analysis
Matrix and Table Visuals
Treemaps, Maps and Other Visuals
Slicers and Interactive Filters
Drill-Down and Drill-Through
Tooltips and Report Navigation
Conditional Formatting
Report Layout and Visual Hierarchy
Dashboard Usability and Design Principles
Publishing Reports to Power BI Service
Refresh, Sharing and Access Considerations
Dashboard Validation and Final Review
24
Advanced Analytics Using Python
13 Topics
Month 5
Time-Series Data and DateTime Indexes
Resampling Data by Day, Month and Year
Rolling Windows and Moving Averages
Trend and Seasonality — Introduction
Customer Segmentation Using Rule-Based Analysis
Cohort Analysis — Introduction
Customer Retention and Churn Metrics
RFM Analysis: Recency, Frequency and Monetary Value
A/B Testing — Introduction
Sampling, Statistical Comparisons and Interpretation
Introductory Forecasting Using Historical Trends
Communicating Analytical Findings
Creating Repeatable Analysis Workflows
25
End-to-End Data Analytics Workflow
16 Topics
Month 6
Understanding Business Requirements
Translating Business Problems into Analytical Questions
Identifying Data Sources and Required Fields
Importing Data from CSV, Excel and SQL
Data Profiling and Quality Assessment
Data Cleaning and Transformation
Exploratory Data Analysis
KPI Definition and Metric Validation
Statistical Analysis and Visualization
Building Reusable Python Analysis Scripts
Integrating Python, SQL and Power BI
Validating Results Against Source Data
Handling Missing, Invalid and Inconsistent Data
Writing Analytical Findings and Recommendations
Preparing a Business Presentation
Final Project Review and Code Improvement
26
Python for Professional Data Analysis
15 Topics
Month 6
Writing Readable and Maintainable Python Code
PEP 8 Coding Standards
Organizing Code into Functions and Modules
Logging and Exception Handling
Debugging Python Scripts
Basic Unit Testing Using unittest or pytest
Managing Dependencies with Virtual Environments
Working with Configuration Files
Git and GitHub Fundamentals
Creating a Project Repository
Writing a Professional README
Documenting Data Sources and Assumptions
Basic Performance Optimization
Responsible Handling of Customer Data
Reproducibility and Version Control
27
Data Analyst Interview Preparation
18 Topics
Month 6
Python Fundamentals Interview Questions
Lists, Dictionaries, Sets and Tuples
Functions, OOP and Exception Handling
NumPy and Pandas Interview Questions
Data Cleaning and EDA Scenarios
SQL Queries, Joins and Aggregations
SQL Subqueries, CTEs and Window Functions
Excel Formulas, Lookups and Pivot Tables
Statistics and Probability Questions
Data Visualization and Chart Selection
Power BI Data Modeling and DAX Questions
KPI and Business Case Questions
Scenario-Based Analytical Problem Solving
Output-Based Python Questions
Resume Preparation and Project Explanation
Mock Technical Interviews
Data Analyst Assessment Test
Final Project Demonstration and Viva
