Data Science is the study of the generalizable extraction of knowledge from data. Being a data Scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and programming languages along with a good understanding of the craft of problem formulation to engineer effective solutions.

This course will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset.

• Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective
• The focus in the treatment of these topics will be a balanced approach on breadth and depth, and emphasis will be placed on the integration and synthesis of concepts and their application to real-time
• To make the learning contextual, real datasets from a variety of disciplines will be

• Most Comprehensive Curriculum
• Trained by passionate and Industry experts
• Each concept will be explained by golden rule

Theory à Example à Software Implementation (R/Python)à Real-Time applicability

• Designed for the Industry
• Live Project
• Placement Assistance

Audience

Any degree. No programming and Statistics knowledge is required.

Duration & Mode of Training

• 3 months, Online Training

Introduction to Data Science – the 3 W’s

• What is Data Science?
• Why now?
• Where Data Science is applicable?

Introduction to Statistics Summarizing Data

• Central Tendency measures – Mean, Median and Mode
• Measures of Variability – Range, Interquartile Range, Standard Deviation and Variance
• Measures of Shape – Skewness and Kurtosis
• Covariance, Correlation Data Visualization
• Histograms
• Pie charts
• Bar Graphs
• Box Plot Probability basics

Parametric and Non parametric Statistical Tests

• ‘f’ Test
• ‘z’ Test
• ‘t’ Test
• Chi-Square test Probability Distributions
• Expected value and variance
• Discrete and Continuous
• Bernoulli Distribution
• Binomial Distribution
• Poisson Distribution
• Normal Distribution
• Exponential Distribution
• Empirical Rule
• Chebyshev’s Theorem

Sampling methods and Central Limit Theorem

• Overview
• Random sampling
• Stratified sampling
• Cluster sampling
• Central Limit Theorem Hypothesis Testing
• Type I error
• Type II error
• Null and Alternate Hypothesis
• Reject or Acceptance criterion
• P-value Confidence Intervals ANOVA
• Assumptions
• One way
• Two way

Introduction to Machine Learning

• What is Machine Learning?
• Statistics (vs) Machine Learning
• Types of Machine Learning
  • Supervised Learning
  • Un-Supervised Learning
  • Reinforcement Learning

Classification

• Nearest Neighbor Methods (knn)
• Logistic

Tree based Models – Decision Tree

• Basics
• Classification Trees
• Regression Trees Probabilistic methods
• Bayes Rule
• Naïve Bayes Regression Analysis
• Simple Linear Regression
• Assumptions
• Model development and interpretation
• Sum of Least Squares
• Model validation
• Multiple Linear Regression Regression Shrinkage Methods
• Lasso
• Ridge
• Advanced Models – Black Box

Association Rules (Market Basket Analysis)

• Apriori Cluster Analysis
• Hierarchical clustering
• DBSCAN
• K-Means clustering Dimensionality Reduction
• Principal Component Analysis
• Discriminant Analysis (LDA/GDA)

Confusion Matrix ROC Curve (AUC) Gain and Lift Chart

Kolmogorov-Smirnov Chart Root Mean Square Error (RMSE) Cross Validation

• Leave one out cross-validation (LOOCV)
• K-fold cross-validation

Introduction to Natural Language Processing Sentiment Analysis

Text Similarity

Introduction

• How is Python different from R
• Installing Anaconda- Python
• Setting up with spyder Datatypes in Python Importing modules Introduction to Strings

String manipulation Control loops

• For
• While
• If else Functions
• Lambda
• apply Numpy Pandas
• Introduction to Dataframes
• Conversion of written R codes into python Scipy-Machine Learning in Python
• Beautiful Soup Matplotlib ..