Overview

Job oriented Data Science certification course to learn data science and machine learning using Python! Python which once was considered as general programming language has emerged as a star of the Data Science world in recent years, owing to the flexibility it offers for end to end enterprise wide analytics implementation. This data science training covers data handling, visualization, statistical modelling and machine learning effectively with practical examples and case studies making it one of the most practical Python online training.

Duration

40 hours

Intended Audience

Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who are not just looking for any Python course, but want Python training with advanced analytics and machine learning skills to head start their career in the field of Data science.

Course outlines:

1.      Basic Statistics & Implementation of Stats Methods in Python

• Basic Statistics - Measures of Central Tendencies and Variance
• Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
• Inferential Statistics -Sampling - Concept of Hypothesis Testing
• Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square
• Important modules for statistical methods: Numpy, Scipy, Pandas

2.      Python: Machine Learning - Predictive Modeling -  Basics

• Introduction to Machine Learning & Predictive Modeling
• Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting
• Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
• Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
• Overfitting (Bias-Variance Trade off) & Performance Metrics
• Feature engineering & dimension reduction
• Concept of optimization & cost function
• Concept of gradient descent algorithm
• Concept of Cross validation (Bootstrapping, K-Fold validation etc)
• Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)

3.      Machine Learning Algorithms & Applications – Implementation in Python

• Linear & Logistic Regression
• Segmentation - Cluster Analysis (K-Means)
• Decision Trees (CART/CD 5.0)
• Ensemble Learning (Random Forest, Bagging & boosting)
• Artificial Neural Networks(ANN)
• Support Vector Machines(SVM)
• Other Techniques (KNN, Naïve Bayes, PCA)
• Introduction to Text Mining using NLTK
• Introduction to Time Series Forecasting (Decomposition & ARIMA)
• Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
• Fine tuning the models using Hyper parameters, grid search, piping etc.