Data Science Program

As a Data Scientist, you have the power to transform raw data into valuable insights, driving strategic decisions and fostering innovation. With your expertise, you will analyze complex datasets, develop predictive models, and uncover patterns that can optimize business processes and enhance decision-making. Whether you're working on data visualization, machine learning algorithms, or integrating data from various sources, the versatility of data science knows no bounds. The journey of becoming a Data Scientist promises growth, challenges, and the fulfillment of turning complex data into actionable, impactful solutions.

Suitable for all backgrounds

The Data Science program is designed for individuals with a strong analytical mindset and a desire to transition into the dynamic field of data science. This program is suitable for:

Anyone with a background in statistics, mathematics, computer science, or a related field and an interest in exploring the world of data analytics.
A seasoned professional looking to expand your skill set and leverage data for strategic decision-making.

From students and professionals in various industries to career changers and tech enthusiasts, data science welcomes learners of all backgrounds and levels of expertise. With dedication, perseverance, and the right mentors, anyone can harness the power of data science and embark on the exciting journey of becoming a Data Scientist.

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, students will work on hands-on projects and assignments to reinforce their learning and gain practical experience in applying data science techniques to real-world datasets.

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 (EDA)

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

Week 9: Basic Statistics

Descriptive Statistics

Mean, Median, Mode
Variance, Standard Deviation
Percentiles and Quartiles

Probability Theory

Basic Probability Concepts
Conditional Probability
Probability Distributions

Week 11: Introduction to Machine Learning

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

Week 13: Supervised Learning - Classification

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

Week 15: Neural Networks

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

Week 17: Model Evaluation and Tuning

Train/Test Split
Cross-Validation
Hyperparameter Tuning

Grid Search
Random Search

Model Selection and Evaluation

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 to Databases and 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: 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 14: Unsupervised Learning

Clustering

K-means Clustering
Hierarchical Clustering

Dimensionality Reduction

Principal Component Analysis (PCA)

Hands-on Project: Customer Segmentation

Week 16: 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 18: Final Project

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

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

The average salary for a Data Scientist in India varies based on experience, skills, and location. Entry-level data scientists can expect to earn between INR 6-10 lakhs per annum. Mid-level data scientists with a few years of experience typically earn around INR 12-20 lakhs per annum, while senior data scientists and those in lead roles can earn upwards of INR 20-30 lakhs per annum.

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

Data Scientists generally have higher salaries compared to other IT and analytics roles due to the advanced skills and expertise required. Their pay is often higher than that of data analysts, business analysts, and sometimes even software engineers, especially in industries where data-driven decision-making is critical.

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

Career growth opportunities for Data Scientists are vast. They can advance to roles such as Senior Data Scientist, Machine Learning Engineer, Data Engineer, AI Specialist, or Analytics Manager. With experience, some may move into senior management positions, such as Chief Data Officer or Head of Data Science, or specialize in cutting-edge areas like deep learning, artificial intelligence, and big data analytics.

Q: What is the future scope of data science 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: What skills are essential to become a successful Data Scientist?

Essential skills for a Data Scientist include:

Proficiency in programming languages such as Python, R, and SQL.
Knowledge of machine learning algorithms and techniques.
Experience with data visualization tools like Tableau, Power BI, and Matplotlib.
Strong analytical and problem-solving skills.
Understanding of statistical analysis and data mining techniques.
Familiarity with big data technologies like Hadoop and Spark.
Good communication and presentation skills to convey insights effectively.

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

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

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

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

A student who has completed a Data Science 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 Scientist

₹10 lakhs per annum

Data Analyst

₹15 lakhs per annum

Machine Learning Engineer

₹8 lakhs per annum

Data Engineer

₹3 to ₹6 lakhs per annum

Business Intelligence Developer

₹6 lakhs per annum

Data Science Manager

₹18 lakhs per annum