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Data Science
6 (REGISTERED)

What Will I Learn?

  • Statistics
  • Probability Theory
  • Data Preprecessing / Data Wrangling
  • Pipeline Building
  • Machine Learning
  • Capstone Project
  • Live Project
  • Resume Preparation
  • Mock Interviews

* Detailed curriculum has been given below

Description

Your Gateway to Becoming a Data Science Expert!

Unlock the power of data-driven decision-making with our industry-focused course.

This program is designed to equip you with essential data science skills, focusing on the core modules that form the foundation of AI and analytics.

Master descriptive and inferential statistics, hypothesis testing, and probability distributions, essential for data-driven insights.

Learn to clean, transform, and structure raw data to make it machine-learning ready using Python and Pandas.

Automate and optimize your data science workflow by structuring end-to-end pipelines for seamless data processing and model deployment.

Dive into Supervised and Unsupervised Learning, feature engineering, model evaluation, and real-world applications of predictive analytics.

Industry-Focused Curriculum – Covers the fundamental data science modules needed for real-world applications.

Hands-On Learning – Work on practical projects to strengthen your skills in data manipulation, statistical analysis, and machine learning.

Beginner-Friendly & Scalable – Ideal for aspiring data scientists, analysts, and engineers looking to break into AI.

Build a Strong Foundation – Develop essential skills to move into advanced data science, deep learning, and AI applications.

Complete the course and gain a certification that boosts your career prospects in data science and analytics.

Enroll now and take your first step into the exciting world of Data Science & Machine Learning!

Who is the target audience?

  • People who already know python and SQL.
  • People who are working in IT industry.
  • Developers who want to re-skill across to Data Science.

Curriculum For This Course

38 Lectures
118 Hours

Statistics 12 Lectures 32:21:29

  • Introduction To Statistics00:15:39
  • Data Collection And Data Preperation02:25:43
  • Sampling, Types Of Sampling And Varinace In Sample02:20:48
  • Data Types03:25:39
  • Descriptive Statistics03:12:23
  • Data Distributions03:17:40
  • Probability Theory02:52:03
  • Central Limit Therem02:45:03
  • Confidence Interval02:48:46
  • Hypothesis Testing02.56:12
  • Correlation And Covariance01:30:00
  • Data Preprocessing04:45:03
  • Hands-On

Data Preprocessing 10 Lectures 02:54:45

  • Type Casting00:05:39
  • Handling Duplicates00:05:43
  • Outlier Analysis00:48:00
  • Zero/Zero Variance00:10:39
  • Handling Missing Values00:35:29
  • Descretization/Binning/Grouping00:15:20
  • Dummy Variable Creation00:17:00
  • Transformation00:10:00
  • Feature Scaling Techniques00:27:00
  • Feature Engineering00:30:00

Building Pipeline For Automation 07 Lectures 01:21:36

  • Separation Of Data Based On Its Types00:05:39
  • Splitting Into Smaller Tasks00:05:43
  • Creation Of Components Of Pipeline00:07:48
  • Understanding Of Scikit-Learn Pipeline00:15:39
  • Understanding Of Scikit-Learn Column Transformer00:10:23
  • Putting All Together 00:09:40
  • Pipeline Implementation00:35:03

Data Science - Life Cycle 07 Lectures 01:30:12

  • Workflow Of Supervised Learning00:15:39
  • Workflow Of Unsupervised Learning00:10:43
  • Model Selection00:05:48
  • Model Evaluation Overview00:15:39
  • Deployment00:15:23
  • Monitoring and Maintenance00:10:40
  • Understanding Overall Workflow00:20:03

Data Science - Unsupervised Learning 07 Lectures 22:01:29

  • Hierarchical Clustering03:48:39
  • K-Means Clustering03:01:43
  • Dimension Reduction - PCA03:07:48
  • Dimension Reduction - SVD03:01:39
  • Association Rules03:00:00
  • Recommendation Engine03:00:00
  • Network Analytics03:30:00

Data Science - Supervised Learning 07 Lectures 29:01:39

  • K - Nearest Neighbour04:01:39
  • Linear Regression05:01:43
  • Logistic Regression04:47:48
  • Support Vector Machine02:01:39
  • Decision Tree04:06:23
  • Ensemble Techniques06:09:40
  • Naive Bayes04:00:03

Data Science - Natural Leanguage Processing 08 Lectures 26:01:25

  • Basics Of NLP03:01:39
  • Dara Preprocessing in NLP03:35:43
  • Feature Engineering in NLP02:07:48
  • Building Pipeline03:01:39
  • Web Scrapping03:23:00
  • Advancements in NLP03:09:40
  • Naive Bayes - Classification03:09:40
  • Transformers06:20:03

Data Science - Time Series Analysis 07 Lectures 04:21:59

  • Survival Analytics01:30:39
  • Time Series - Data Types00:20:43
  • Forecasting Strategies01:07:48
  • Components Of Time Series00:45:39
  • Data Partition00:29:23
  • Forecasting Of Errors00:31:40
  • Seasonal Index00:25:03

Data Science - Deep Learning 07 Lectures 29:56:21

  • Introduction To Deep Learning01:41:39
  • Anatomy Of Neural Networks03:51:43
  • ANN - Regression03:07:48
  • ANN - Classification03:01:39
  • CNN - Object Classification02:51:23
  • CNN - Object Detection03:09:40
  • RNN - Prediction, Sentiment Analysis04:24:03

PyTorch 07 Lectures 07:10:15

  • Introduction To PyTorch01:01:39
  • Modules Of PyTorch01:01:43
  • Important Functions01:07:48
  • Project Flow in PyTorch01:01:39
  • Transfer Learning01:04:23
  • Hyper Parameter Tuning01:09:40
  • Capstone Project I01:10:03

TensorFlow 07 Lectures 07:01:47

  • Introduction To TensorFlow00:20:39
  • Modules Of TensorFlow01:01:43
  • Important Functions01:07:48
  • Project Flow in TensorFlow01:01:39
  • Transfer Learning02:12:45
  • Hyper Parameter Tuning03:09:40
  • Capstone Project II00:00:00

Capstone Project I 10 Meetings 15:00

  • Building POC15:00
  • Problem and Business Understanding15:00
  • Data Understanding and Preparation15:00
  • Model Building15:00
  • Model Evaluation15:00
  • Model Deployment15:00
  • Monitoring and Maintenance15:00

Capstone Project II 10 Meetings 15:00

  • Building POC15:00
  • Problem and Business Understanding15:00
  • Data Understanding and Preparation15:00
  • Model Building15:00
  • Model Evaluation15:00
  • Model Deployment15:00
  • Monitoring and Maintenance15:00

Live Project 20 Meetings 20:00

  • Building POC15:00
  • Problem and Business Understanding15:00
  • Data Understanding and Preparation15:00
  • Model Building15:00
  • Model Evaluation15:00
  • Model Deployment15:00
  • Monitoring and Maintenance15:00

Instructors

  • 4.7 .7 Average rating
  • 25,182 Reviews
  • 11,085 Students
  • 2 Courses
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Data Science Crash Course For IT Professional

Hi! I'm Indira Kumar, a passionate Data Scientist with four years of experience in AI, machine learning, and data-driven solutions. Over the years, I've worked on complex AI models, optimized data pipelines, and developed cutting-edge applications that drive real business impact. Now, I'm embarking on a new journey—building a company that leverages the power of AI to transform industries. My mission is to help businesses harness the true potential of data, from predictive analytics to intelligent automation. Having worked with diverse datasets and industries, I’ve gained deep expertise in developing scalable AI solutions, from computer vision models to NLP-based applications. I believe in making AI accessible, efficient, and impactful. Through my work, I’ve mentored aspiring data scientists, collaborated with industry leaders, and built AI solutions that redefine possibilities. My goal with this new venture is to empower businesses and individuals with the best AI-driven insights and solutions.