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| Item Details | Price | ||
|---|---|---|---|
Language: English
Instructors: TransOrg Analytics
Job Assistance Program in other words, is a complete package. It is carefully designed to transform anyone into an industry ready data scientist, not only by the means of knowledge, but also through practical exposure, constant guidance and most importantly through our expert feedback on crucial interview skills.
| Module 1- Python for Data Science | |||
| Introduction | |||
| Module Introduction (2:00) | Preview | ||
| Intuition (6:00) | Preview | ||
| Constructs (12:00) | Preview | ||
| Set-up | |||
| Data Science Tools (3:00) | Preview | ||
| Introduction to Jupyter notebooks (9:00) | Preview | ||
| Basics of Python | |||
| Variables (30:00) | Preview | ||
| Operations (31:00) | Preview | ||
| Python Operations Jupyter Notebook | |||
| Quiz - Basics | |||
| Conditional Programming-1 (9:00) | |||
| Conditional Programming-2 (5:00) | |||
| Conditional Programming-3 (14:00) | |||
| Functions in Python-1 (20:00) | |||
| Functions in Python-2 (18:00) | |||
| Conditional Programming and Functions in Python Jupyter Notebook | |||
| Data Structures | |||
| Data Structures-1 (27:00) | |||
| Quiz - Lists | |||
| Data Structures-2 (7:00) | |||
| Data Structures-3 (17:00) | |||
| Quiz - Sets | |||
| Data Structures-4 (18:00) | |||
| Quiz - Dictionaries | |||
| Python data structures Jupyter notebook | |||
| Loops-1 (18:00) | |||
| Loops-2 (3:00) | |||
| Loops-3 (4:00) | |||
| Python Loops Jupyter Notebook | |||
| Libraries in Python | |||
| Numpy-1 (15:00) | |||
| Numpy-2 (7:00) | |||
| Numpy Jupyter Notebook | |||
| Matplotlib (43:00) | |||
| Matplotlib Jupyter Notebook | |||
| Quiz - Numpy, Matplotlib | |||
| Quiz - End of Chapter Quiz | |||
| Coding Assignment - Python Basics | |||
| Module 2 - Pandas | |||
| Pandas 1 - Basics (23:00) | |||
| Pandas 2 - Row, Column Operations (18:00) | |||
| Pandas 3 - Accessing data (22:00) | |||
| Pandas 4 - Slicing and Sorting (34:00) | |||
| Pandas 5 - Grouping and Aggregation (28:00) | |||
| Pandas 6 - Pivot Tables (10:00) | |||
| Pandas 7 - Joins (18:00) | |||
| Pandas 8 - Concat (12:00) | |||
| Pandas 9 - Merge (18:00) | |||
| Pandas -10 (12:00) | |||
| Pandas Quiz | |||
| Pandas Jupyter notebooks | |||
| Coding Assignment - Python Basics 2 | |||
| Introduction to Statistics | |||
| Introduction to Statistics (6:00) | |||
| Types of variables (6:00) | |||
| Measures of Central Tendency and Spread (10:00) | |||
| Quiz 1 - Basics | |||
| Measuring Position (5:00) | |||
| Histograms (4:00) | |||
| Quiz 2 - Position | |||
| Types of Distributions (5:00) | |||
| Normal Distribution (14:00) | |||
| Quiz 3 - Distributions | |||
| Central Limit Theorem (12:00) | |||
| Quiz 4 - Central Limit Theorem | |||
| Confidence Intervals (6:00) | |||
| T- Distribution (6:00) | |||
| Quiz 5 - Confidence Intervals and T distribution | |||
| Hypothesis Testing - 1 (9:00) | |||
| Hypothesis Testing - 2 (5:00) | |||
| Examples on Hypothesis Testing (13:00) | |||
| Quiz 6 - Hypothesis testing | |||
| Tutorial - Hypothesis testing (4:00) | |||
| Tutorial - Sampling (12:00) | |||
| Tutorial - Descriptive Primer (10:00) | |||
| Final Quiz | |||
| Statistics Assignment | |||
| Module 4 - Introduction to Machine Learning | |||
| Introduction (28:00) | |||
| Learning process (21:00) | |||
| Somethink to think about! | |||
| Quiz 1 ML Basics | |||
| EDA_FE (31:00) | |||
| Quiz 2- Basics | |||
| Feature Selection (13:00) | |||
| How a model learns (8:00) | |||
| Measuring Performance (7:00) | |||
| Model Performance (11:00) | |||
| Bias-Variance (14:00) | |||
| Quiz 3 - How a model works? | |||
| Visualisation (27:00) | |||
| Quiz 4 EDA | |||
| Feature Engg Demo (24:00) | |||
| Feature Scaling (13:00) | |||
| Quiz 5 Feature Engineering | |||
| Module 3 Assignment - EDA | |||
| Module 3 Assignment EDA and Feature Engineering | |||
| Module 5 - Supervised Learning -1 | |||
| Module Intro (14:00) | |||
| Introduction to Linear Regression (14:00) | |||
| Model training - Linear Regression (16:00) | |||
| Model evaluation - Linear Regression (10:00) | |||
| Regularisation - linear regression (15:00) | |||
| Assumptions of Linear Regression (11:00) | |||
| Locally weighted Linear regression (9:00) | |||
| Demonstration - Linear Regression (10:00) | |||
| Linear Regression Quiz -1 | |||
| Linear Regression Quiz - 2 | |||
| Introduction to Logistic Regression (13:00) | |||
| Model Training - Logistic Regression (15:00) | |||
| Model evaluation - Logistic Regression (15:00) | |||
| Demonstration - Logistic Regression (13:00) | |||
| Multiclass Classification (12:00) | |||
| Logistic Regression Quiz 1 | |||
| Logistic Regression Quiz 2 | |||
| Module 6 - Supervised Learning 2 | |||
| Introduction to Decision Trees (27:00) | |||
| Classification Trees (5:00) | |||
| Bagging (12:00) | |||
| Boosting (12:00) | |||
| Module 7 - Unsupervised Learning | |||
| K-Means Clustering (16:00) | |||
| Demo-K-Means (16:00) | |||
| Hierarchical Clustering (13:00) | |||
| Association Rule Mining (19:00) | |||
| Demo-Association Rule Mining (16:00) | |||
| Recommendation Systems (16:00) | |||
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