Course Overview:
The Full Stack Data Science Online Instructor-Led Course is an immersive and comprehensive program designed for individuals aspiring to become proficient data scientists capable of handling the end-to-end data science process. From data acquisition and preprocessing to model building, deployment, and interpretation, this course covers the entire data science lifecycle. Delivered through online instructor-led sessions, participants will gain theoretical knowledge, hands-on skills, and real-world application experience.
Curriculum
- 3 Sections
- 65 Lessons
- 10 Weeks
Expand all sectionsCollapse all sections
- Full Stack Data Science Course33
- 2.0Introduction to Analytics and Data Science
- 2.1Lingos around Artificial Intelligence
- 2.2Descriptive Statistics
- 2.3Measures of Spread – What & Why of Range , applications & Limitations of range, how to compute range
- 2.4Percentile in Statistics
- 2.5Difference between standard deviation and percentiles
- 2.6Normal Distribution
- 2.7How to Find a distribution Pattern in Excel
- 2.8Skewed Distribution, Positive and Negative Skewed Distribution, Computing Mean for Skewed Distribution
- 2.9Q & A for Measures of Central Tendency & Distribution
- 2.10Recap of Distribution patterns in Statistics
- 2.11Quiz – Normal and Skewed Distribution
- 2.12Standard Normal Distribution
- 2.13Bi Modal Distribution
- 2.14Uniform Distribution
- 2.15What & Why of Kurtosis ,Type of Kurtosis , For. to calc. Kurtosis, Difference bet Skewnes & Kurtosis
- 2.16Types of Sampling Techniques
- 2.17What and Why of Inferential Statistics
- 2.18Sampling Distribution
- 2.19Central Limit Theorem
- 2.20Hypothesis Testing
- 2.21Single Sample Hypothesis t Test
- 2.22Hypothesis Testing Quiz10 Minutes5 Questions
- 2.23Hypothesis test – Single Sample Z and T test
- 2.24Hypothesis Testing – Paired T Test & how to perform the same test in Python
- 2.25Type 1 and Type 2 Error in Hypothesis Testing
- 2.26Case Study – Two Independent Sample T test – Unequal Variance Test
- 2.27One Way Anova Testing Intuition
- 2.28Case Study – One Way ANOVA Testing
- 2.29Case Study – Two-Way Anova Without Replication
- 2.30Case Study – Two-Way Anova With Replication
- 2.31Chi Square Test for Association
- 2.32Statistics Assignments
- Python Training Course33
- 3.1Introduction to Python
- 3.2Variables in Python
- 3.3Python Variables Quiz10 Minutes12 Questions
- 3.4Data Types in Python
- 3.5Python Data Types Quiz10 Minutes15 Questions
- 3.6Data Structures in Python
- 3.7Python Data Structures Quiz10 Minutes15 Questions
- 3.8Control Limit & Hypothesis Testing Assignment Solutions
- 3.9Dictionary in Python
- 3.10Sets in Python
- 3.11Arrays and Matrices in Python – Deep Dive
- 3.12Data Visualization
- 3.13Pandas
- 3.14Joining Dataframes and Reshaping the data in Pandas
- 3.15Performing Data Imputation in Python
- 3.16Linear Regression model Assumption
- 3.17Linear Regression Introduction
- 3.18Linear Regression Contd…
- 3.19K Fold Cross Validation
- 3.20Interpreting Linear Regression Model Output
- 3.21Linear Regression Intuition
- 3.22Probability and Odds Ratio
- 3.23Logistic Regression Intuition
- 3.24When to use Accuracy, Precision and Recall Metrics to evaluate Classifier model performance
- 3.25Random Forest and Gradient Boosting Machine
- 3.26Training Random Forest, GBM in Python. Hyperparameter Tuning
- 3.27Unsupervised Machine Learning, k-means hierarchical clustering
- 3.28Neural Network implementation using Keras in Python
- 3.29Activation Functions in Neural Network
- 3.30Backpropagation Part 1
- 3.31Backpropagation – Part 2
- 3.32Neural Network Question and Answer
- 3.33Convolutional Neural Network
- Machine Learning Interview Questions3