Data Science +Power BI Program

This program covers the complete data analytics workflow, including data collection, cleaning, and preparation. Learners will analyze data to identify patterns and trends, perform exploratory data analysis, and interpret results for business use. The course emphasizes data visualization and interactive dashboard creation using Power BI, along with presenting insights to support data-driven decision-making.

Suitable for all backgrounds

This program is designed for learners from both technical and non-technical backgrounds. While prior technical knowledge is beneficial, it is not mandatory. The course builds an analytical mindset and gradually develops core skills in data handling, analysis, and visualization using Data Science concepts and Power BI through step-by-step instruction and hands-on practice.

All our programs include essential add-ons focusing on soft skill development. because technical expertise alone is not enough for success in the corporate world. Our courses focus on developing essential soft skills such as effective communication, teamwork, and leadership. This comprehensive approach ensures you build the right attitude and confidence to thrive in any professional environment.

Professional Development Workshop

Comprehensive soft skills training
Interpersonal skills enhancement sessions
Professionalism and workplace etiquette modules

Communication Mastery Program

Advanced presentation techniques
Team communication strategies and collaboration methods

Placement Assisstance Services

Tailored resume writing workshops
Personal branding and LinkedIn profile optimization guidance
Interview preparations via mock interviews and assessments

Group Dynamics Training

Techniques for active and impactful participation
Strategies for effective communication and teamwork

Facilitation Skills

Gain proficiency in corporate tools like Jira, Confluence, Retrium, Mural etc
Leadership skills for effective facilitation
Facilitation techniques for fostering productive discuss

Throughout the program, learners will work on hands-on projects and assignments using real-world datasets to apply Data Science concepts and Power BI techniques. The course also provides access to guided tutorials and learning resources for continuous practice and support.

Week 1: Introduction to Data Science

What is Data Science?
The Data Science Process
Applications of Data Science
Overview of Data Science Tools
Setting up the Environment (Anaconda, Jupyter Notebook)

Week 3: Python Data Structures

Lists and Tuples
Dictionaries and Sets
String Manipulation
List Comprehensions
Practice Problems and Exercises

Week 5: Advanced Pandas

Data Cleaning
Handling Missing Values
Data Transformation
Grouping and Aggregation
Merging and Joining DataFrames
Practice Problems and Exercises

Week 7: Exploratory Data Analysis

Understanding the Dataset
Summary Statistics
Identifying Patterns and Trends
Data Visualization Techniques
Case Study: EDA on a Real-World Dataset

WEEK 9 - Basics of Statistics

Descriptive Statistics
- Mean, Median, Mode
- Variance, Standard Deviation
- Percentiles and Quartiles
Probability Theory
- Basic Probability Concepts
- Conditional Probability
- Probability Distributions

WEEK 11 - Power BI basics and Data Preparation

Introduction to Power BI
Connecting to Data
Shaping & Transforming Data
Data Profiling & Query Management
Merging & Appending Queries

WEEK 13 -Visualization, dashboards and Optimization

Visualizing Data
Customization & Interactions
Filters & Bookmarks
Power BI Performance Optimization
External Tools & Optimization

WEEK 15 : Supervised Learning - Regression

Linear Regression
- Simple Linear Regression
- Multiple Linear Regression
Model Evaluation Metrics (MSE, RMSE, R-squared)
Hands-on Project: Predicting House Prices

WEEK 17 - Neural Network

Introduction to artificial neural networks (ANN)
Activation functions (ReLU, Sigmoid, Tanh)
Backpropagation and gradient descent
Optimization techniques (Adam, RMSprop, SGD)

WEEK 19 - Unsupervised Learning

Clustering
- K-means Clustering
- Hierarchical Clustering
Dimensionality Reduction
- Principal Component Analysis (PCA)
Hands-on Project: Customer Segmentation

WEEK 21 - Final Project

Project Definition and Dataset Selection
Data Collection and Preparation
Exploratory Data Analysis
Model Building and Evaluation
Presenting Findings and Insights

Week 2: Python Basics

Introduction to Python
Data Types and Variables
Basic Operators
Control Flow (if, for, while)
Functions and Modules

Week 4 : Introduction to NumPy and Pandas

Introduction to NumPy
- Arrays and Matrices
- Indexing and Slicing
- Basic Operations
Introduction to Pandas
- Series and DataFrames
- DataFrame Operations
- Importing and Exporting Data

Week 6: Data Visualization with Matplotlib and Seaborn

Introduction to Matplotlib
- Basic Plots (line, bar, scatter)
- Customizing Plots
Introduction to Seaborn
- Statistical Plots
- Plot Aesthetics
Creating Dashboards and Interactive Visualizations

Week 8: Introduction Database & SQL

Basics of SQL
- SELECT, INSERT, UPDATE, DELETE
- WHERE, JOIN, GROUP BY, HAVING
Connecting Python to Databases
Querying Databases with Pandas

WEEK 10 - Inferential Statistics

Hypothesis Testing
Null and Alternative Hypotheses
p-values and Significance Levels
Confidence Intervals
t-tests, chi-square tests, ANOVA

WEEK 12 - Data Modeling and DAX

Creating a Data Model
Table Relationships
Introduction to DAX
Writing DAX Formulas
DAX Practice

WEEK 14 - Introduction to Machine Learning

What is Machine Learning?
Supervised vs Unsupervised Learning
Overview of Machine Learning Algorithms
Introduction to Scikit-Learn

WEEK 16 - Supervised Learning - Classification

Logistic Regression
Decision Trees
Random Forests
Model Evaluation Metrics (Accuracy, Precision, Recall, F1-score)
Hands-on Project: Classifying Iris Species

WEEK 18 - Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Lemmatization, Stop words)
Bag of Words, TF-IDF, Word2Vec
Language models (GPT, BERT)
Sentiment analysis, text classification, named entity recognition

WEEK 20 - Model Evaluation and Tuning

Train/Test Split
Cross-Validation
Hyperparameter Tuning
- Grid Search
- Random Search
Model Selection and Evaluation

Q: What is the average salary of a Data Analyst in India?

The average salary for a Data Analyst in India varies based on experience, skills, and location. Entry-level data analysts can expect to earn between INR 4-8 lakhs per annum. Mid-level data analysts with a few years of experience typically earn around INR 8-12 lakhs per annum, while senior data analysts and those in lead roles can earn upwards of INR 15-20 lakhs per annum.

Q: How does the salary of a Data Analyst compare to other IT and analytics roles in India?

Data Analysts generally have competitive salaries compared to other IT and analytics roles. Their pay is comparable to that of business analysts and sometimes higher depending on the industry and specialization. Specialized skills and experience in high-demand areas can lead to higher salaries.

Q: What are the career growth opportunities for a Data Analyst in India?

Career growth opportunities for Data Analysts are extensive. They can advance to roles such as Senior Data Analyst, Data Scientist, Business Analyst, Data Engineer, or Analytics Manager. With experience, some may move into senior management positions or specialize in areas such as machine learning, big data, and business intelligence.

Q: What is the future scope of data analysis in India?

The future scope of data analysis in India is highly promising. With the increasing importance of data-driven decision-making across industries, the demand for skilled data analysts is expected to grow. Sectors such as finance, e-commerce, healthcare, and technology are heavily investing in analytics, leading to numerous job opportunities.

Q: Which industries in India are hiring Data Analysts?

Data Analysts are in demand across various industries in India, including:

Information Technology and Services
Banking and Financial Services
E-commerce and Retail
Healthcare
Telecommunications
Manufacturing
Media and Entertainment
Consulting and Professional Services

Q: What skills are essential to become a successful Data Analyst?

Essential skills for a Data Analyst include:

Proficiency in data analysis tools and software such as Microsoft Excel, SQL, R, and Python.
Knowledge of data visualization tools like Tableau, Power BI, and QlikView.
Strong analytical and problem-solving skills.
Understanding of statistical analysis and data mining techniques.
Familiarity with database management systems.
Good communication and presentation skills to convey insights effectively.

Q: How strong is the competition in the field of data analysis in India?

The competition in the field of data analysis is significant due to the growing interest in analytics and the increasing number of professionals entering this domain. However, the demand for skilled data analysts is also high, and those who continuously upgrade their skills and stay updated with the latest technologies can find ample opportunities.

Q: What educational background is preferred for a career in data analysis?

A background in computer science, statistics, mathematics, economics, or related fields is preferred. Most Data Analysts hold a Bachelor’s or Master’s degree in these areas. Additionally, relevant certifications and hands-on experience through internships or projects are highly beneficial.

A student who has completed a Data Analyst program opens up a wide range of career possibilities in various industries. Some potential career paths and entry level salary in Indian job market are:

Data Analyst (Power BI)

₹7 - ₹8 lakhs per annum

Business Analyst

₹4 to ₹6 lakhs per annum

Power BI Developer

₹6 to ₹12 lakhs per annum

Market Research Analyst

₹3 to ₹6 lakhs per annum

Healthcare Data Analyst

₹3 to ₹7 lakhs per annum

Supply Chain Analyst

₹4 to ₹8 lakhs per annum