BSATES EdTech Foundation

Advanced Data Science

(276 Reviews)

Join our data science course to earn certificates, enhance analytical skills, interpret data, and make effective, data-driven decisions for career growth.

Durattion: 6 Months

100+ Recruitment Partners

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Skill Level

Beginner

Time to Complete

6 Months

Projects

50+ Projects

Prerequisites

None

About Data Science

Our Data Science course equips you with skills in data analysis, machine learning, and statistical modeling using Python, Flask, ChatGPT, NumPy and SQL. Gain hands-on experience with real-world projects, data visualization, and AI techniques, earning industry-recognized certification to advance your career in data-driven roles.

Tools You Will Learn in Data Science

COURSE CURRICULUM

Master Skills with Detailed Curriculum

Module 1: Introduction to Data Science

What is Data Science?

Applications of Data Science

Data Science vs. Data Analytics vs. Machine Learning

Setting Up the Development Environment (Python, Flask, ChatGPT, NumPy and SQL)

Module 2: Mathematics & Statistics for Data Science

Linear Algebra (Vectors, Matrices, Eigenvalues)

Probability and Statistics

Hypothesis Testing

Descriptive and Inferential Statistics

Correlation and Regression Analysis

Module 3: Programming for Data Science

Python Basics: Variables, Data Types, Control Structures

Data Manipulation with Pandas

NumPy for Scientific Computing

Data Cleaning and Preprocessing

Module 4: Data Wrangling & Preprocessing

Handling Missing Values

Data Transformation Techniques

Feature Engineering

Data Normalization & Standardization

Handling Categorical Data

Module 5: Exploratory Data Analysis (EDA)

Data Visualization with Matplotlib & Seaborn

Identifying Patterns and Outliers

Correlation Analysis

Data Distribution and Statistical Insights

Module 6: Machine Learning Fundamentals

Introduction to Supervised & Unsupervised Learning

Bias-Variance Tradeoff

Model Selection and Evaluation Metrics

Train-Test Split & Cross-Validation

Module 7: Supervised Learning Algorithms

Linear Regression & Logistic Regression

Decision Trees & Random Forest

Support Vector Machines (SVM)

K-Nearest Neighbors (KNN)

Gradient Boosting (XGBoost, LightGBM)

Module 8: Unsupervised Learning Algorithms

Clustering (K-Means, Hierarchical, DBSCAN)

Principal Component Analysis (PCA)

Association Rule Learning (Apriori, Eclat)

Module 9: Deep Learning & Neural Networks

Introduction to Neural Networks

Deep Learning with TensorFlow & Keras

Convolutional Neural Networks (CNN)

Recurrent Neural Networks (RNN) & LSTMs

Transformers & Advanced Architectures

Module 10: Data Science Projects & Case Studies

Hands-on Projects (Fraud Detection, Recommendation Systems, Predictive Analytics)

Industry Case Studies

Resume Building & Interview Preparation

Why Get Our Data Science Certification

Doubt Clearing Sessions

Industry Relevant Projects

Assignment Evaluation & Solutions

PW Lab For Your Code Practice

Industry Experts Trainers

Career Guidance Sessions

JOB ASSISTANCE PROGRAM

Fast-Track Your Career with Job Assistance Program

Soft Skills Sessions

Aptitude Training

Resume Building Sessions

LinkedIn Profile Building

Mock Interviews

Job Assistance

How it Works?

Choose a programme of your interest

Receive personal counselling

Complete application & payment

Get certified & qualify for placement

ELIGIBILITY CRITERIA

Understand the Eligibility Criteria for Course Enrollment

B.E./B.Tech - Computer Science, Information Technology,Electronics, Electronics & Telecommunications, Electrical, Electrical & Electronics
BSc/MSc - Computer Science, Information Technology
BCA/MCA
Final Year Students - Expecting to Graduate in 2023
Fresh Graduates - Graduated in 2021/2022
Working Professionals - Graduated in 2020

FREQUENTLY ASKED QUESTIONS

Most Popular Questions About Our Online Courses

A Data Science course teaches data analysis, machine learning, and statistical modeling to extract insights from data and solve real-world problems.

Anyone interested in data-driven decision-making, including students, working professionals, and software engineers looking to switch to Data Science roles.

Basic programming knowledge is helpful, but beginner-friendly courses start from fundamentals. Python and R are commonly used in the course.

The course primarily covers ython, Flask, ChatGPT, NumPy and SQL for data analysis, machine learning, and statistical computing.

You will learn Pandas, NumPy, Matplotlib, Scikit-Learn, TensorFlow, PyTorch, SQL, and cloud platforms like AWS and Google Cloud.

Topics include Data Preprocessing, Machine Learning, Deep Learning, Data Visualization, NLP, Big Data, and Model Deployment.

Yes, after successfully completing the course, you will receive an industry-recognized certification.

Yes, you will work on real-world projects, including predictive modeling, recommendation systems, fraud detection, and NLP applications.

You can enroll through the course provider’s website by filling out the application form and meeting the eligibility requirements.