Data Science

Data science is very much popular in today’s world scenario as there is a huge amount of data generated each day in different fields such as BFSI, Healthcare and Telecom. This training encompasses a conceptual understanding of Statistics, Machine Learning and Deep Learning using the Python and R programming languages.

Introduction to Data Science

• What is Data Science?
• Data science lifecycle
• Use Cases/applications/examples
• DS tools and technology

Python Programming

• Installation
• Python 2.7 Vs 3.4
• Python programming fundamentals
• Data types and structures, variables, Control flows, and functions
• Python libraries
• Numpy, Pandas, SciKitLearn, MatPlotLib

R Programming

• Introduction to R
• Vectors
• Matrices
• Factors
• Data Frames
• Lists

Data Extraction, Wrangling and Exploration

• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis(EDA)
• Data Structures in Pandas - Series and Data Frames

Probability

• Basic Probability
• Conditional Probability
• Properties of Random Variables
• Expectations
• Variance
• Entropy and cross-entropy
• Covariance and correlation
• Estimating probability of Random variable
• Understanding standard random processes

Inferential Statistics

• Estimating parameters of a population using sample statistics
• Hypothesis testing and confidence intervals
• T-tests and ANOVA
• Correlation and regression
• Chi-squared test

Descriptive Stats

• Compute and interpret values like: Mean, Median, Mode, Sample, Population and Standard Deviation.
• Compute simple probabilities.
• Explore data through the use of bar graphs, histograms and other common visualizations.
• Investigate distributions and understand a distributions properties.
• Manipulate distributions to make probabilistic predictions on data.

Data visualization

• Bar Graph, Histogram, Pi Chart, Line Chart, Box (Whisker) Plot, Scatter Plot, Heat map

Basic Machine Learning Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• KNN (K- Nearest Neighbours)
• K-Means Clustering
• Naïve Bayes
• Dimensionality Reduction

Advanced algorithms

• Random Forests
• Dimensionality Reduction Techniques
• Support Vector Machines
• Gradient boosting

Introduction to Deep Learning

• Tensor flow
• Neural Networks
• Biological Neural Networks
• Understand Artificial Neural Networks
• Building an Artificial Neural Network
• How ANN works
• Image recognition
• Image classification

Sentiment Analysis

Text Mining

Natural Language Processing(NLP)

Time Series

• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied
• Implement respective ETS model for forecasting