Data Science Online Training

nandhu — Thu, 28 May 2020 13:49:57 +0000

Learn Data Science Online with Free tutorials

Data Science Online Training is the art of uncovering the insights and trends in data.

To Learn Data science online is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structured and unstructured data.

data science training

Who can Learn Data Science online?

In order to become a data scientist, one should have immense interest in the data and in identifying the hidden knowledge in the data. Interest and possession of basic knowledge in mathematics and statistical principles help understand and design new techniques. Coding is an essential requirement of a data scientist. Domain knowledge helps in understanding the problem at hand and paves the way for an optimized solution. Anyone who can cover these areas can attend data science bootcamp.

Advantages of Data Science Bootcamp

Data Science is greatly in demand.

Data Science offers abundance of Positions.

A Highly Paid Career.

Data Science is Versatile.

Data Science Makes Data Better.

Data Scientists are Highly Prestigious.

No More Boring Tasks only challenging and creative tasks.

Data Science Makes Products Smarter.

Data Science can Save Lives by providing insights into health care Data Science can make you better equip with.

What we should learn Data Science Online ?

Data science is an interdisciplinary field focused on extracting knowledge from data sets, which are typically large. The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization. As such, it incorporates skills from computer science, mathematics, statistics, information visualization, graphic design, and business.

How will be the career in Data Science bootcamp and what next?

For three years in a row (2016-19), data science is named as the number 1 job in the US by Glassdoor

Over the last decade, there has been a huge explosion in the amount of data generated and retained by organizations. Almost every company or business today relies heavily on data, thus giving rise to a massive demand for qualified and experienced data scientists. And with more and more data being captured across the globe than ever before, the demand for expertise to extract valuable and actionable insights from that data is going to only grow by leaps and bounds.

DATA SCIENCE ONLINE TRAINING COURSE CONTENT

Learn Data Science Online Using R Language

Data science Introduction

Data Science motivating examples — Money ball, okcupid, Nate Silver, Netfilx, LinkedIn,
Introduction to Analytics, Types of Analytics,
Introduction to Analytics Methodology
Analytics Terminology, Analytics Tools
Introduction to Big Data
Introduction to Machine Learning

Data Science Using R Language

What is Data Science?
Common Terms in Analytics
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Overview of analytics tools & their popularity
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Why R for data science?

Introduction to R

Introduction to R
Install R & R studio
Perform basic operations in R using command line
Learn the use of IDE R Studio
Use the â€˜R helpâ€ feature in R

Introduction to R programming

Variables in R
Matrices
List
Scalars
Vectors
Data frames
Using c, Cbind, Rbind, attach and detach functions in R
Factors

Data Manipulation in R

Data sorting
Cleaning data
Recoding data
Merging data
Slicing of Data
Apply functions

Functions and operators in R

Topics
Numerical functions
Character functions
Operators in R
Arithmetic operator
Relational operator
Logical operator
Assignment operator

Decision making and looping statements in R

If loop
For loop
While loop
Break, next and pass statement

Data Import in R

Reading Data from excel, csv and txt files
Writing Data
Basic SQL queries in R
Connecting to the database
Dealing with Date values

Exploratory Data Analysis

Box plot
Histogram
Pareto charts
Pie graph
Line chart
Scatterplot
Developing Graphs

Introduction To Predictive Modelling

Concept of model in analytics and how it is used?
Common terminology used in analytics & modelling process
Popular modelling algorithms
Different Phases of Predictive Modelling

Data Exploration For Modelling

Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers data
Visualize the data trends and patterns

Linear Regression: Solving Regression Problems

Introduction – Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
Assess the overall effectiveness of the model
Interpretation of Results

Logistic Regression: Solving Classification Problems

Introduction – Applications
Building Logistic Regression Model
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
Validation of Logistic Regression Models
Standard Business Outputs (ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

Supervised Learning: Decision Trees

Decision Trees – Introduction – Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration
Decision Trees – Validation
Overfitting – Best Practices to avoid

Supervised Learning: Ensemble Learning

Concept of Ensembling
Random forest (Logic, Practical Applications)
SUPERVISED LEARNING: SUPPORT VECTOR MACHINES
Motivation for Support Vector Machine & Applications
Interpretation of Outputs and Fine tune the models with hyper parameters

Supervised Learning: Knn

What is KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyper parameters

Supervised Learning: NaÃ¯ve Bayes

Concept of Conditional Probability
Bayes Theorem and Its Applications
NaÃ¯ve Bayes for classification
Applications of NaÃ¯ve Bayes in Classifications
TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS
Introduction – Applications
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Unsupervised Learning: Segmentation

What is segmentation & Role of ML in Segmentation?
K-Means Clustering
Expectation Maximization

Learn Data Science Online Using Python

Introduction to data science using python:

What is Data Science?
Common Terms in Analytics
Types of problems and business objectives in various industries
Overview of analytics tools & their popularity
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Why Python for data science?

Python: Essentials

Overview of Python- Starting with Python
Introduction to installation of Python
Introduction to Python IDE’s
Understand Jupyter notebook
Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Basic Operations
Reading and writing data
Simple plotting
Control flow & conditional statements
How to create class and modules and how to call them?

Scientific Distributions Used In Python For Data Science

Numpy, pandas, matplotlib, scikitlearn etc
ACCESSING/IMPORTING AND EXPORTING DATA USING PYTHON MODULES
Importing Data from various sources (Csv, txt, excel etc)
Viewing Data objects – subsetting, methods
Exporting Data to various formats
Important python modules: Pandas

Data Manipulation â€“ Cleansing â€“ Munging Using Python Modules

Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

Data Analysis â€“ Visualization Using Python

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas etc)

Introduction To Statistics

Basic Statistics – Measures of Central Tendencies and Variance
Building blocks – Probability Distributions – Central Limit Theorem
Inferential Statistics -Sampling – Concept of Hypothesis Testing
Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
Important modules for statistical methods: Numpy, Scipy, Pandas
Principle component Analysis (PCA)

DATA SCIENCE ONLINE TRAINING WITH FREE TUTORIAL

DATA SCIENCE BOOTCAMP VIDEO

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DATA SCIENCE BOOTCAMP INSTRUCTOR

Raghunath

Close to 2 decades of experience in academia and IT industry. Served technology giant Satyam Computers in a senior position and led huge training and IT services projects. Initiated 3 startups successfully. Recent startup was was a digital auditing app in retail, space widely used by retail organizations. The startup was acquired recently. Worked as a Resource Person for various academic institutions. Currently Imparting Training on machine learning and Data Science thru R and Python. And currently, on the board of 2 startup initiatives as strategic advisor for their technology implementation.
Working in the HR domain for Data Analytics

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