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Data Analytics Course

Job-Ready Certification Program

paid course

The Data Analytics with Python Program is a comprehensive, industry-focused training designed to equip students with practical skills required to succeed in today’s data-driven job market.

Organizations across industries rely on data to make strategic decisions. This program prepares learners to collect, clean, analyze, visualize, and interpret data using industry-standard tools such as Python, SQL, Excel, Tableau, Power BI, and basic Machine Learning techniques.

The course emphasizes hands-on learning, real-time projects, and business-oriented problem solving, ensuring students are job-ready by the end of the program.

COURSE OBJECTIVE

The primary objectives of this program are:

To build strong foundations in Python programming for data analysis
To develop expertise in data cleaning, manipulation, and transformation using Pandas & NumPy
To enable students to write efficient SQL queries for data extraction
To create interactive dashboards using Tableau and Power BI
To apply statistical concepts for business decision-making
To understand the fundamentals of Machine Learning for analytics
To complete real-world capstone projects for portfolio building
To prepare students for interviews through practical case studies and mock sessions

CAREER OPPORTUNITIES

(JOB ROLES)

After successful completion of this course, learners can apply for the following roles:

Data Analyst
Business Analyst
Junior Data Scientist
Reporting Analyst
MIS Executive
Operations Analyst
Marketing Analyst
Financial Data Analyst
HR Analyst

These roles are available across industries including IT, Banking, E-commerce, Healthcare, EdTech, Manufacturing, and Consulting.

🟡 Data Visualization using Python

Introduction to Matplotlib
Line plots, Bar charts, Scatter plots
Histograms and Boxplots
Introduction to Seaborn
Heatmaps and correlation matrix
Customizing charts (titles, labels, legends)
Visual storytelling techniques

🔵 Business Intelligence Tools - Tableau

Connecting data sources
Creating charts and visualizations
Filters and parameters
Calculated fields
Dashboard creation
Publishing dashboards

🔴 Statistics for Data Analytics

Descriptive statistics (mean, median, mode)
Measures of dispersion (variance, standard deviation)
Probability basics
Normal distribution
Correlation analysis
Hypothesis testing (t-test, p-value)
Business KPI analysis

🟢 Basic Machine Learning for Analysts

Introduction to Machine Learning
Supervised vs Unsupervised learning
Data preprocessing
Train-test split
Linear Regression
Logistic Regression
Decision Trees
Model evaluation (Accuracy, Precision, Recall, Confusion Matrix)
Overfitting and bias-variance concept

🟡 Capstone Projects

Retail sales data analysis project
HR attrition analysis
Customer segmentation project
Loan risk prediction model
End-to-end SQL + Python + Dashboard project
Resume-ready GitHub portfolio development

SYLLABUS COVERAGE

🟢 Python Programming Fundamentals

Introduction to Python
Variables and Data Types (int, float, string, boolean)
Operators (Arithmetic, Logical, Comparison)
Conditional Statements (if, else, elif)
Loops (for, while, break, continue)
Functions (user-defined functions, lambda functions)
Data Structures (Lists, Tuples, Sets, Dictionaries)
File Handling (reading and writing CSV/TXT files)
Exception Handling (try, except, finally)
Working with Jupyter Notebook and VS Code

🟡 NumPy for Numerical Computing

Introduction to NumPy arrays
Array creation methods (zeros, ones, arange, linspace)
Indexing and slicing arrays
Array reshaping and broadcasting
Mathematical operations (mean, median, variance, standard deviation)
Basic liExcel for Data Analytics near algebra operations (matrix multiplication, dot product)

🔵 Pandas for Data Manipulation

Introduction to Series and DataFrames
Importing data from CSV, Excel, and SQL
Data inspection and exploration
Handling missing values (dropna, fillna)
Removing duplicates
Filtering and sorting data
GroupBy operations and aggregations
Pivot tables
Merging and joining datasets
DateTime operations
Feature engineering basics

🔴 SQL for Data Analysis

Introduction to databases
SELECT, WHERE, DISTINCT
Filtering using AND, OR, IN, BETWEEN, LIKE
ORDER BY and LIMIT
Aggregate functions (COUNT, SUM, AVG, MIN, MAX)
GROUP BY and HAVING clause
Joins (INNER JOIN, LEFT JOIN, RIGHT JOIN)
Subqueries
Window functions (RANK, ROW_NUMBER)
Connecting SQL with Python

🟢 Excel for Data Analytics

Data cleaning techniques
Advanced formulas (IF, VLOOKUP, XLOOKUP, INDEX-MATCH)
Conditional formatting
Pivot tables and pivot charts
Data visualization using Excel charts
Creating interactive Excel dashboards

🧰 Tools & Technologies

Limited Seats Available – Enroll Now!

📞 Call / WhatsApp: 9052237153
🖥️ Mode: Online or Offline available

CONTACT TO ENROLL

learning outcomes

By the end of the program, students will be able to:

🔹 Technical Skills

Write clean and efficient Python code for data analysis
Handle large datasets using Pandas and NumPy
Perform data cleaning, transformation, and feature engineering
Write complex SQL queries including joins and aggregations
Build dashboards using Excel, Tableau, and Power BI
Perform Exploratory Data Analysis (EDA)
Apply statistical methods like hypothesis testing and correlation analysis
Build and evaluate basic machine learning models
Interpret model results and explain them in business terms

🔹 Business & Analytical Skills

Identify business problems and translate them into data questions
Derive meaningful insights from raw datasets
Present findings through structured reports and dashboards
Communicate technical insights clearly to non-technical stakeholders

🔹 Career Readiness

Build a professional portfolio with real-world projects
Prepare a strong analytics-focused resume
Gain confidence in technical interviews and case studies
Understand industry workflows and project execution lifecycle

why this course is industry relevant

This curriculum is aligned with current job market requirements where companies expect:

Python + SQL proficiency
Dashboard development skills
Business-oriented thinking
Strong data visualization ability
Practical project experience

The course focuses not just on tools, but on problem-solving and insight generation, which is what employers truly value

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