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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
Get Enrolled Now

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

1
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)
2
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
3
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
4
Module 4: Data Wrangling & Preprocessing
Handling Missing Values
Data Transformation Techniques
Feature Engineering
Data Normalization & Standardization
Handling Categorical Data
5
Module 5: Exploratory Data Analysis (EDA)
Data Visualization with Matplotlib & Seaborn
Identifying Patterns and Outliers
Correlation Analysis
Data Distribution and Statistical Insights
6
Module 6: Machine Learning Fundamentals
Introduction to Supervised & Unsupervised Learning
Bias-Variance Tradeoff
Model Selection and Evaluation Metrics
Train-Test Split & Cross-Validation
7
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)
8
Module 8: Unsupervised Learning Algorithms
Clustering (K-Means, Hierarchical, DBSCAN)
Principal Component Analysis (PCA)
Association Rule Learning (Apriori, Eclat)
9
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
10
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
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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.