Data Science

To manage whopping amount of the data, data scientists are needed who are the most enthusiastic people. It is the undoubtedly emerging field in data analysis which has great link with the upcoming data software that are being prepared for the improvisation of data management.

 Data Analyst
 Data Scientist
 M/L engineer
 Data Science Lead

Any graduate who wanst to learn how to apply machine learning to real world datasets.
Prerequisites are some programming, a bit of statistics, and machine learning theory.

Introduction to Data science
 What is data science?
 How is Data Science different from BI and Data Analyst
 Who are Data Scientist?
 What skill sets are required to become a Data Scientist ?
 What is machine learning?
 Statistics and Mathematics for Data Science
Introduction to Python
 Introduction to Anaconda
 Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
 Jupyter Notebook Installation
 Jupyter Notebook Introduction
 Variable Assignment
 Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
 Creating, accessing, and slicing tuples
 Creating, accessing, and slicing lists
 Creating, viewing, accessing, and modifying dicts
 Creating and using operations on sets
 Basic Operators: ‘in’, ‘+’, ‘*’
 Functions
 Classes and Objects
 Packages
Python Essentials for Data Science
 Introduction to NumPy
 Data Manipulation with Pandas
 Visualization with Matplotlib
 NLTK
 Keras / TensorFlow / Theano
 Scikit-Learn
NumPy
 Introduction to Numpy
 Numpy Arrays
 Quick Note on Array Indexing
 Numpy Array Indexing
 Numpy Operations
 Numpy Exercises Overview
 Numpy Exercises Solutions
 Understanding Data Types in Python
 The Basics of NumPy Arrays
 Computation on NumPy Arrays: Universal Functions
 Aggregations: Min, Max, and Everything In Between
 Computation on Arrays: Broadcasting
 Comparisons, Masks, and Boolean Logic
 Fancy Indexing
 Sorting Arrays
 Structured Data: NumPy’s Structured Arrays
Pandas
 Pandas -Python for Data Analysis
 Introduction to Pandas
 Series
 DataFrames
 Missing Data
 Groupby
 Merging Joining and Concatenating
 Operations
 Data Input and Output
 Introducing Pandas Objects
 Data Indexing and Selection
 Operating on Data in Pandas
 Handling Missing Data
 Hierarchical Indexing
 Combining Datasets: Concat and Append
 Combining Datasets: Merge and Join
 Aggregation and Grouping
 Pivot Tables
 Vectorized String Operations
 Working with Time Series
Seaborn -Python for Data Visualization
 Introduction to Seaborn
 Link to Seaborn Documentation
 Distribution Plots
 Categorical Plots
 Matrix Plots
 Grids
 Regression Plots
 Style and Color
 Seaborn Exercise Overview
 Seaborn Exercise Solutions
Matplotlib
 Simple Line Plots
 Simple Scatter Plots
 Visualizing Errors
 Density and Contour Plots
 Histograms, Binnings, and Density
 Customizing Plot Legends
 Customizing Colorbars
 Multiple Subplots
 Text and Annotation
 Customizing Ticks
 Customizing Matplotlib: Configurations and Stylesheets
 Three-Dimensional Plotting in Matplotlib
Scientific computing with Python (Scipy)
 SciPy and its Characteristics
 SciPy sub-packages
 SciPy sub-packages –Integration
 SciPy sub-packages – Optimize
 Linear Algebra
 SciPy sub-packages – Statistics
 SciPy sub-packages – Weave
 SciPy sub-packages – I O
 Scikit-Learn Approach
 Scikit – Learn Approach Built – in Modules
 Scikit – Learn Approach Feature Extraction
 Scikit – Learn Approach Model Training
 Scikit – Learn Grid Search and Multiple Parameters
Mathematical and Business Statistics Concepts for Data Science
 Mean, Mode Median
 Standard deviation, Variance, Correlation Analysis, Skewness, Quartile
 Linear Algebra, Probability, Optimization Theory
 Time series data representation
 Some Common Terms Used in Statistics
 Data Distribution: Central Tendency, Percentiles, Dispersion
 Histogram
 Bell Curve
 Hypothesis Testing
 Chi-Square Test
 Correlation Matrix
 Inferential Statistics
 Data type
 ICategorical Data (Nominal, Ordinal)
 Numerical Data (Discrete, Continuous, Interval, Ratio)
 Why Data Types are important?
 Statistical Methods
Descriptive Statistics
 Measures of Frequency: * Count, Percent, Frequency .
 Measures of Central Tendency. * Mean, Median, and Mode
 Measures of Dispersion or Variation. * Range, Variance, Standard Deviation
 Measures of Position. * Percentile Ranks, Quartile Ranks.
Sampling
 Different types of sampling
 Simple Random sampling:
 Systematic sampling
 Stratified sampling
 data distribution
 inferential statistics
Test of Hypothesis
 Null Hypothesis formulation
 Alternative Hypothesis
 Type I and Type II errors
 Power Value
 One tail and two tail
 T-TEST’s
 ANOVA
 MANOVA
 Chi Square Test
 Kendall Chi Square
 Kruskal-Wallis Rank Test Chi Square
 Mann-Whitney, Chi Square
 Wilcoxon, Chi Square
Data cleaning process Quality check and Data Profiling
 Unsupervised data
 PCA Regression Scores for Supervised data
 Noise Data detecting
 Data cleaning with Regression Residual
 Data Transformation
 data wrangling
 Data Mining
 Data Profiling
 Model Validation and Testing
 Data science & Business Analytics
 Basic Probability for Business issues
Machine Learning
 What Is Machine Learning
 Key terminology
 Key tasks of machine learning
 Steps in developing a machine learning application
 Categories of Machine Learning
 Qualitative Examples of Machine Learning Applications
Predictive Analytics
 Different type of Predictive Analytics – prediction, forecasting, optimization, segmentation etc..
 Supervised Learning
 Unsupervised Learning
 Time series Analysis –forecasting
Supervised Learning
 Regression
 Classification
Regression
 Linear Regression & Logistic: A Model-Based Approach
 Linear Regression Theory
 Model selection Updates for SciKit Learn
 Linear Regression with Python /R
 Linear Regression Project Solution
 Regression fundamentals: Data and Models
 Feature selection in Model building
 Evaluating over fitting via training/test split
 Training
 Finding best-fit lines with linear regression
 Weighted linear regression
 Shrinking coefficients
 Ridge regression
 The bias/variance trade off
 Example: using linear regression
 Tree-based regression
 Building trees with continuous and discrete features
 Using CART for regression
 Tree pruning
Classification
 Analyzing the sentiment of reviews: A case study in Classification
 Classification fundamentals : Data and Models
 Understanding Decision Trees & Naive Bayes
 Feature selection in Model building
 Linear classifiers
 Decision boundaries
 Training and evaluating a classifier
 False positives, false negatives, and confusion matrices
 Classifying with k-Nearest Neighbours
 Distance measurements
 Classifying with decision trees
 Tree construction
 Testing and storing the classifier
 Example: using decision trees
 Classifying with Bayesian decision theory
 Classifying with conditional probabilities
 Classification with the AdaBoost algorithm
 Classification imbalance
 Recommendation
Clustering
 Clustering System Overview
 Data and Models
 Feature selection in Model building
 Clustering and similarity ML block diagram
 Unsupervised Learning – Recommendation
 Recommender systems ML
Deep Learning
 Deep Learning: Searching for Images
 Searching for images: A case study in deep learning
 Learning very non-linear features with neural networks
 Application of deep learning to computer vision
 Deep learning performance
 Demo of deep learning model on Image Net data
 Deep learning ML block diagram
 K Nearest Neighbors
 KNN Theory
 KNN Project Overview
 KNN Project Solutions
Decision Trees and Random Forests
 Introduction to Tree Methods
 Decision Trees and Random Forest with Python
 Decision Trees and Random Forest Project Overview
 Decision Trees and Random Forest Solutions
Support Vector Machines
 SVM Theory
 Support Vector Machines with Python
 SVM Project Overview
 SVM Project Solutions
 K Means Clustering
 K Means Algorithm Theory
 K Means with Python
 K Means Project Overview
 K Means Project Solutions
 Bisecting k-means
 EM algorithm
 Example: clustering
 The Apriori algorithm
 Frequent item set generation
 Association rule generation
 Finding association rules in voting
 Principal Component Analysis (PCA)
 Understand the basics of RL and its applications in AI
 Q-learning algorithms
Principal Component Analysis
 Principal Component Analysis
 PCA with Python
Natural Language Processing
 Natural Language Processing Theory
 NLP with Python
 NLP Project Overview
 NLP Project Solutions
TensorFlow
 What is TensorFlow?
 Changes with TensorFlow
 TensorFlow Installation
 TensorFlow Basics
 MNIST with Multi-Layer Perceptron
 TensorFlow with ContribLearn
 Tensorflow Project Exercise Overview
 Tensorflow Project Ex+ercise – Solutions
Artificial Intellegence
 Turing Machines & Turing Test
 AI Intelligence Agents & Environments
 AI Learning Types
 AI Problem Solving
 Single-State Problem
 Multi-State Problem
 Water-Jug Problem
 Maze Problem
 Queens Problem
 AI Search Algorithms
 Brute Force Search
 BFS,DFS, Uniform Cost Search
 Heuristic Search
 Hill Climbing Search
 Travelling Salesman Problem
Model Selection & Boosting
 Model selection
 XGBoost
Time series
 Date and Time Data Types and Tools
 Time Series Basics
 Date Ranges, Frequencies, and Shifting
 Time Zone Handling
 Periods and Period Arithmetic
 Resampling and Frequency Conversion
 Time Series Plotting
 Moving Window Functions
 Performance and Memory Usage Notes
 Auto Regression, Moving Average,
 Multiplicative, ARMA, Additive Model
R Programming
 Installing R & R-studio
Data Types
 Vector
 Array
 Matrix
 Data Frame & List
 Factors
R Connection – Interfaces
 Reading Tabular Data
 Textual Data Format
 Compress file – gzip , bzip2
 Connection to web
Control Structure
 If-Else
 For loop
 While Loop
 Repeat , Next , Break
Functions
 Packages & Libraries
 Writing custom functions
Date & Time
R Objects
Loop Functions
 apply
 lapply
 mapply
 tapply
 split
Logistic Regression in R
 Reason for Logistic Regression
 The Logistic Transform
 Logistic Regression Modelling
 Model Optimisation
 Understanding ROC Curve
 Default Modelling using Logistic Regression in R Lang
Decision Trees
 Theory of Entropy & Information Gain
 Stopping Rules
 Cross Validations for Overfitting Problem
 Pruning as a Solution for Overfitting
 Ensemble Learning
 Bootstrap Aggregation
 Random Forests
 Intrusion Detection in IT Network
Linear Regression in R
 Covariance and Correlation
 Multivariate Analysis
 Hypothesis Testing
 Limitations of Regression
 Business Case: Managing Credit Risk
 Loss Given Default using Linear Regression
Support Vector Machine
 Classification as a Hyper Plane Location Problem
 Motivation for Linear Support Vectors
 Quadratic Optimization
 Non Linear SVM
 Kernel Functions
 Default Modelling using SVM in R
Introduction to big-data
 Big data and analytics?
 Leverage Big data platforms for Data Science
 Introduction to evolving tools e.g Spark
 Machine learning with Spark
Analytical Visualisation with Tableau and SAS
 Why is it important for Data-Analyst
 Tableau workbook walkthrough
 Instruction of creation of your own workbooks
 Demo of few more workbooks
Introduction to cloud and Big-Data computing over cloud
 Amazon Guide to Creating an AWS Account
 Quick Note on AWS Security
 EC2 Instance Set-Up
 AWS WITH ml
PySpark
 Introduction to Spark and Python
 RDD Transformations and Actions
We are Offering
 Mock interviews questions and case studies
 Guidance to prepare resumes
 Information on companies and industry trends on data science
Work Shop
SQL
SAS
BIGDATA Hadoop and Pyspark
Tableau
Case Studies, Capstone Project.

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