Customized ONLINE Classes available.
Course Name | Starting Date | Time | Faculty Name |
---|---|---|---|
Data Science | 21-DEC-2020 | 11:00am | Mr.N.Krishna |
Data Science | 23-DEC-2020 |
11:00am |
Mr.P.Srinivas |
The biggest challenge of the information explosion happening these days is the Data administration and management. Data Science course gets you the in depth understanding of statistical techniques for the data analysis. The course allows one to bring up their basic database knowledge and make it apply to the more advanced level of data science. The career of the future is Data Science which is a very much typically needed for the current data analysis of IT field.
Get to know about the business analysis and business intelligence
Data collection and data mining
To learn working with Tableau
Expertise in R-lang and data exploration to R
Clear understanding of Decision trees.
Detailed knowledge of the statistical tools for Data analysis
Machine Learning
Big data technologies
To learn running non-parametric tests
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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