DATA SCIENCE
Data Science & Data Analytics Overview
Introduction to Data Science
1
What is Data Science
2
Why Data Science
3
Components of data Science
4
Roles of Data Scientist
5
Applications of data science
6
Why Python for Data Science
Introduction to Data Analytics
1
Types of Analytics
2
Analytics Lifecycle
Introduction to Big Data
1
What is Big Data?
2
3 Vs of Big Data
3
Big Data Architecture
4
Big Data Technologies
5
Hadoop Ecosystem
What is Machine Learning
1
Types of Machine Learning
Data
1
Types of Data
2
Data Collection Types
3
Forms of Data & Sources
4
What is Data Architecture
5
Components of Data Architecture
6
OLTP vs OLAP
Descriptive Statistics & Distributions:
1
Central Tendency (mean, median and mode)
2
Interquartile Range, Variance, Standard Deviation
3
Z—Score/T—Score
4
Co-variance, Correlation
5
Binomial Distribution, Normal Distribution
6
Bar Chart, Histogram, Box whisker plot
7
Dot-plot, Line plot, Scatter Plot
Inferential Statistics
1
Central Limit Theorem
2
Confidence Interval and z-distribution table
3
Statistical Significance, Hypothesis testing
4
P-value, One-tailed and Two-tailed Tests
5
Chi-Square Goodness of Fit Test
6
F- Statistic, Kurtosis, Skewness
7
Sampling, Why Sampling, Sampling Methods
Data Pre- processing / Wrangling / Munging Methods
1
Preprocessing Introduction
2
How to preprocess your data
3
Data Generation Techniques (Custom data)
4
Data Imputation techniques (Dealing with Missing Values)
5
Outlier detection methods
Encoding and Decoding techniques :-
1
Label encoding
2
One hot encoding
Data Normalization/ Transformation/Scaling methods :-
1
Z- score
2
Min Max
Dimensionality Reduction methods :-
1
Advantages of dimensionality reduction methods
2
Principle Component Analysis
3
Linear Discrimination Analysis
4
Singular Valued Decomposition
Feature Selection Methods
1
How to select the right data
2
Which are the best features to use
Filter Methods :-
1
F test
2
Mutual Information
3
Variance threshold
Wrapper Methods :-
1
Forward Search
2
Recursive feature elimination
Embedded Methods :-
1
Lasso linear regression
2
Tree based methods
3
A feature selection case study
Overviews of Python
1
Python Overview, About Interpreted Languages
2
Using Variables, Keywords, Built-in Functions, Data Types
3
Strings Different Liberals
4
Math Operators and Expressions
5
String Formatting
6
flow Control and Loops
Python Data Structures
1
Lists
2
Tuples
3
Indexing and Slicing
4
Iterating through a Sequence
5
Functions for all Sequences
6
Operators and functions for Sequences
7
List Comprehensions
8
Generator Expressions
9
Dictionaries and Sets
10
Dictionary Comprehension
11
Functions
12
Modules
13
Regular Expressions
14
File I/O operations
Databases Access using Python
1
Creating a Database with MySQL
2
CRUD Operations
3
Creating a Database Object.
NumPy/SciPy
1
Introduction to NumPy
2
Mathematical Functions
3
Copies & Views
4
Creating and Printing Ndarray
5
Class and Attributes of Ndarray
6
Basic Operations
Pandas and Data Frames
1
Introduction to Pandas
2
Understanding Data Frame
3
Data Operations
4
Creating Data Frames
5
Grouping Sorting
6
Plotting Data
7
Creating Functions
8
Converting Different Formats
9
Combining Data from Various Formats
10
Slicing/Dicing Operations
11
File Read and Write Support
12
Plotting using Matplotlib and Seaborn
Exploratory Data Analysis
1
Statistical Data Analysis
2
Fixing missing values
3
Finding outliers
4
Data quality check
5
Feature transformation
Data Visualization (Matplotlib & Seaboarn) :-
1
Categorical to Categorical, Categorical to Quantitative, Quantitative to Quantitative
Bi-Variate data analysis (Hypothesis Testing) :-
1
Categorical and Quantitative, Categorical to Categorical, Quantitative to Categorical, Quantitative to Quantitative
Data Visualization
1
Matplotlib and
2
Seaborn
Machine Learning
1
Supervised Learning
2
Unsupervised Learning
3
Reinforce Learning
Regression Analysis
1
What is Classification/Regression and its use cases?
2
Introduction to Regression/Prediction
3
Simple Linear Regression algorithm
4
Multiple Linear Regression
5
Evaluation metrics (R-Square, Adj R-Square, MSE, RMSE), Hypothesis testing
6
Multiple linear regression
7
Train/Test Split, Hypothesis testing formal way
Classification Techniques
Decision Tree Classifier :-
1
What is Decision Tree?
2
Mathematical implementation of decision tree classifier
3
Algorithm for Decision Tree Induction
4
How to build Decision trees
5
Selecting attribute selecting measures using Information Gain, Gini Index, Gain Ratio techniques
6
Tree Pruning
7
Use Case using Decision Tree classifier
Random Forest Classifier :-
1
What are Random Forests
2
Features of Random Forest
3
Mathematical implementation of Random Forest Classifier
4
Out of Box Error Estimate and Variable Importance
5
Use Case using Random Forest classifier
Naive Bayes Classifier :-
1
Conditional Probability
2
Mathematical implementation of Random Forest Classifier
3
Bayesian classifier algorithm
4
Use Case using Naive Bayes classifier
Logistic Regression :-
1
Logistic Regression Overview
2
Algorithm and Mathematical explanation
3
Use Case using Logistic Regression for binary classification
Support Vector Machines :-
1
Introduction to SVMs
2
Vectors Overview
3
Decision Surfaces
4
Linear SVMs
5
The Kernel Trick
6
Non-Linear SVMs
7
The Kernel SVM
Model Evaluation
1
Accuracy measurements
2
Precision, Recall, Precision — Recall Thread-off
3
AUC Score, ROC Curve
4
Train/Validation/Test split, K-Fold Cross Validation
5
The Problem of Over-fitting (Bias-Variance thread-off)
Regularization
1
Learning Curve
2
Regularization (Ridge, Lasso and Elastic-Set)
3
Feature selection
4
Hyper Parameter Tuning (Grid-SearchCV, Randomized-SearchCV)
Un-Supervised Learning Techniques
1
What is Clustering/Grouping and its use cases?
2
Similarity Metrics
3
Distance Measure Types: Euclidean, Manhattan, Cosine Measures
4
Creating predictive models
5
K-Means flustering
6
Hierarchical clustering algorithm
7
(simple link, complete link and average link)
8
DB-Scan algorithm
Metrics for Clustering :-
1
Silhouette
2
Elbow Method
3
Homogeneity
4
completeness
5
V-measure
Association Rule Mining
1
Introduction to Association Rule Mining
2
Support, Confidence measures
3
Appriori algorithm for ARM
4
FP-Growth Algorith for ARM
5
Eclat algorithm for ARM
6
What is Recommendation Engine & it’s working?
7
Recommendation Use-case
Time Series Analysis
1
What is Time Series data?
2
Time Series variables
3
Different components of Time Series data
4
Visualize the data to identify Time Series Components
5
Implement ARIMA model for forecasting
6
Exponential smoothing models
7
Identifying different time series scenario based on which different Exponential Smoothing model can be applied
8
Implement respective model for forecasting
9
Visualizing and formatting Time Series data
10
Plotting decomposed Time Series data plot
11
Applying ARIMA and ETS model tor Time Series forecasting
12
Forecasting for given Time period
13
Illustrate the working and implementation of different ETS models
14
Forecast the data using the respective model
Natural Language Processing
1
Vector Space Model
2
Bag of Words
3
TF—IDF
4
Tokenization
5
Stop word Removal
6
Stemming
7
POS tagging
8
Lemmatization
9
Synset and Wordnet
10
Topic Modelling
11
Document Classification
12
Document Clustering
13
Web Scraping
14
Word Embedding
Deep Learning
Introduction to Artificial Neural Networks :-
1
Deep Learning Overview
2
Neural Networks Introduction
3
Brain vs Neuron
4
Detailed ANN
5
Activation functions
6
How does ANN work and learn?
7
Gradient Decent
8
Stochastic Gradient Decent
9
Backpropagation
Convolutional Neural Networks :-
1
Convolution Operation
2
Relu Layers
3
What is Pooling and Flattening
4
Full connection
5
Soft Max vs Cross Entropy
Tensor Flow :-
1
Programming a neural network in TensorFlow
2
Programming a neural network — multilayer perceptron in TensorFlow
Keras :-
1
Introduction to Keras – a convenient way to code neural networks
2
What is Convolutional neural network
3
How does a CNN work?
CNN and RNNs :-
1
Creating a CAN front scratch
2
What are RNN- Introduction to RNNs
3
Recurrent Neural Networks RNN in Python
4
LSTMs for beginners — understanding LSTMs
5
Long short-term memory NN LSTM in Python
Hadoop with Spark
1
Introduction to Bid Data
2
Introduction to Hadoop Environment
3
Architecture of HDFS
4
Map Reduce Programming
5
Introduction to PySpark
6
Spark Clusters and Data Frames
7
Linear Regression using PySaprk
8
Building Classification using PySaprk
9
Developing Clustering Models using PySpark
10
NLP PySaprk
Environment Setup
1
Python 3.6X
2
Anaconda Distribution (Jupiter Notebook, Spyder) IDE
About Instructor:
1
Dr Venkat working as a Data Science consultant and a reputed organization
2
Certified by DELL EMC Data Science and Big Data Associate
3
Completed his Ph.D., in Data Science and MTech from CST
4
Having 10 years of Research experience in Data Science and NLP
5
Having 20 years of Experience in Teaching various high-end technologies
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