LEARN DATA SCIENCE

• Recap of Statistics and Probability basics starting from basic coin toss experiments.
•  Get familiar with Probability Terminologies.
•  Understand types of probability – Conditional, Marginal and Joint probabilities, Random Variables, Probability Distributions – Normal, Gaussian distributions.
•  Statistical Tests – t-test, F-test, Chi-Square test.
•  Probability of  Success, probability of failure, Odds Ratio, ROC Curve.

• Why Statistics and Probability?
•  Measurements of Data, categorization of Data in depth.
•  Understand Statistical Terminologies – mean, median, mode, Standard Deviation, Correlation Coefficients, population, sample.
•  Different statistical metrics – what to use and when?
•  Distance Metrics – Euclidean, Manhattan.
•  Standard Deviation, Variance, Bias-Variance Trade-off – Underfitting, Overfitting.
•  Outlier Analysis – What and how to spot Outliers, Inter-Quartile Range, Box & Whisker plot, Scatter plot, Cook’s Distance.
•  Correlation – Pearson Correlation, Positive & Negative Correlation.
•  Confusion Matrix – Precision, Recall, Specificity, F1 Score.
•  Regression Error Metrics – MSE, RMSE, MAE,MAPE.

• Why learn R?, Intro to RStudio, Installation and Walkthrough
•  Importance of R in Statistics and Probability, Operations using R
•  R Basics such as loops, data types, etc.,
•  Data Import and Manipulation in R
•  Exploratory Data Analysis using R
•  Data Visualization in R, Reading and Writing data from/to files using R programming.

• Python Overview, Python IDEs, Installation of Anaconda and Walkthrough
•  Keywords, variables, Built-in functions, intro to Pandas, numpy libraries
•  Operations using Python, Python Basics such as loops, data types, etc.,
•  Data import using Python, Data Manipulation in Python
•  EDA using Python, Data Visualization, Reading and Writing data from/to files using Python programming.

• What is data visualization?, Understand Importance of Data Visualization, Good vs Bad Data Visualization
•  Get to know the Tools and languages for Data Visualization, Best Practices for Data Visualization, Intro to Tableau for Data Science
•  Data Analysis using Python, Tableau

• Intro to SQL, basics of SQL
•  Advanced SQL

• Overview of Machine Learning, types of learning techniques
•  Applications of ML, Advantages of moving to ML over traditional processes
•  Discussion and hands-on over numerous Machine Learning Techniques: 
•  Supervised Learning: Linear Regression, Logistic Regression, Naïve Bayes, Decision Trees, Random Forests, K-Nearest Neighbours.
•  Unsupervised Learning - K-Means, Clustering
•  What algorithm to choose when?

Busting common myths about Data Science and Data Science jobs in organizations.

• Introduction and overview of Text Mining
• Different kinds of Analyses – Discussion and Lab: Data Cleaning, Bag of Words, TFIDF, Sentiment Analysis.

• Introduction, Evolution and Advantages of Neural Networks
• Understand when are neural networks better over ML and vice-versa.
• Understand when and how to use Neural Networks.
• Discussion and hands-on of multiple kinds of Neural Networks: Perceptron, MLP, ANN, CNN, RNN.
• Working with Images using CNN.