DATA SCIENCE WITH R PROGRAMMING COURSE

The following topics are covered here

Module 1 - Introduction to Data Science with Python

• Installing Python Anaconda distribution
• Python native Data Types
• Basic programing concepts
• Python data science packages overview

Module 2 - Python Basics: Basic Syntax, Data Structures

• Python Objects
• Math & Comparision Operators
• Conditional Statement
• Loops
• Lists, Tuples, Strings, Dictionaries, Sets
• Functions
• Exception Handling

Module 3 - Numppy Package

• Importing Numpy
• Numpy overview
• Numpy Array creation and basic operations
• Numpy Universal functions
• Selecting and retrieving Data
• Data Slicing
• Iterating Numpy Data
• Shape Manupilation
• Stacking and Splitting Arrays
• Copies and Views : no copy, shallow copy , deep copy
• Indexing : Arrays of Indices, Boolean Arrays

Module 4 - Pandas Package

• Importing Pandas
• Pandas overview
• Object Creation : Series Object , DataFrame Object
• View Data
• Selecting data by Label and Position
• Data Slicing
• Boolean Indexing
• Setting Data

Module 5 - Python Advanced: Data Mugging with Pandas

• Applying functions to data
• Histogramming
• String Methods
• Merge Data : Concat, Join and Append
• Grouping & Aggregation
• Reshaping
• Analysing Data for missing values
• Filling missing values: fill with constant, forward filling, mean
• Removing Duplicates
• Transforming Data

Module 6 - Python Advanced: Visualization with MatPlotLib

• Importing MatPlotLib & Seaborn Libraries
• Creating basic chart : Line Chart, Bar Charts and Pie Charts
• Ploting from Pandas object
• Saving a plot
• Object Oriented Plotting : Setting axes limits and ticks
• Multiple Plots
• Plot Formatting : Custom Lines, Markers, Labels, Annotations, Colors
• Satistical Plots with Seaborn

Module 7 - Exploratory Data Analysis: Case Study

The following topics are covered here

Module 1: Introduction to Statistics

• Two areas of Statistics in Data Science
• Applied statistics in business
• Descriptive Statistics
• Inferential Statistics
• Statistics Terms and definitions
• Type of Data
• Quantitative vs Qualitative Data
• Data Measurement Scales

Module 2: Harnessing Data

• Sampling Data, with and without replacement
• Sampling Methods, Random vs Non-Random
• Measurement on Samples
• Random Sampling methods
• Simple random, Stratified, Cluster, Systematic sampling.
• Biased vs unbiased sampling
• Sampling Error
• Data Collection methods

Module 3: Exploratory Analysis

• Measures of Central Tendencies
• Mean, Median and Mode
• Data Variability : Range, Quartiles, Standard Deviation
• Calculating Standard Deviation
• Z-Score/Standard Score
• Empirical Rule
• Calculating Percentiles
• Outliers

Module 4: Distributions

• Distribtuions Introduction
• Normal Distribution
• Central Limit Theorem
• Histogram - Normalization
• Other Distributions: Poisson, Binomial et.,
• Normality Testing
• Skewness
• Kurtosis
• Measure of Distance
• Euclidean , Manhattan and Minkowski Distance

Module 5: Hypothesis & computational Techniques

• Hypothesis Testing
• Null Hypothesis, P-Value
• Need for Hypothesis Testing in Business
• Two tailed, Left tailed & Right tailed test
• Hypothesis Testing Outcomes : Type I & II erros
• Parametric vs Non-Parametric Testing
• Parametric Tests , T - Tests : One sample, two sample, Paired
• One Way ANOVA
• Importance of Parametric Tests
• Non Parametric Tests : Chi-Square, Mann-Whitney, Kruskal-Wallis etc.,
• Which Test to Choose?
• Ascerting accuracy of Data

Module 6: Correlation & Regression

• Introduction to Regression
• Type of Regression
• Hands on of Regression with R and Python.
• Correlation
• Weak and Strong Correlation
• Finding Correlation with R and Python

The following topics are covered here

Module 1: Machine Learning Introduction

• What is Machine Learning
• Applications of Machine Learning
• Machine Learning vs Artificial Intelligence
• Machine Learning Languages and platforms
• Machine Learning vs Statistical Modelling

Module 2: Machine Learning Algorithms

• Popular Machine Learning Algorithms
• Clustering, Classification and Regression
• Supervised vs Unsupervised Learning
• Application of Supervised Learning Algorithms
• Application of Unsupervised Learning Algorithms
• Overview of modeling Machine Learning Algorithm : Train , Evaluation and Testing.
• How to choose Machine Learning Algorithm?

Module 3: Supervised Learning

• Simple Linear Regression : Theory, Implementing in Python (and R), Working on use case.
• Multiple Linear Regression : Theory, Implementing in Python (and R), Working on use case.
• K-Nearest Neighbors : Theory, Implementing in Python (and R), KNN advantages, Working on use case.
• Decision Trees : Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on use case.

Module 4: Unsupervised Learning

• K-Means Clustering: Theory, Euclidean Distance method.
• K-Means hands on with Python (and R)
• K-Means Advantages & Disadvantages

The following topics are covered here

Module 1: Advanced Machine Learning Concepts

• Tuning with Hyper parameters.
• Popular ML algorithms,
• Clustering, classification and regression,
• Supervised vs unsupervised.
• Choice of ML algorithm
• Grid Search vs Random search cross validation

Module 2: Principle Component Analysis (PCA)

• Key concepts of dimensionality reduction
• PCA theory
• Hands on coding.
• case study on PCA

Module 3: Random Forest - Ensemble

• Key concepts of Randon Forest
• Hands on coding.
• Pros and cons.
• case study on Random Forest

Module 4: Support Vector Machine (SVM)

• Key concepts of Support Vector Machine.
• Hands on coding.
• Pros and Cons.
• case study on SVM

Module 5: Natural Language Processing (NLP)

• Key concepts of NLP.
• Hands on coding.
• Pros and Cons.
• Text Processing with Vectorization
• Sentiment analysis with TextBlob
• Twitter sentiment analysis

Module 6: Naïve Bayes Classifier

• Key concepts of Naive Bayes.
• Hands on coding.
• Pros and Cons
• Naïve Bayes for text classification
• New articles tagging

Module 7: Artificial Neural Network (ANN)

• Basic ANN network for Regression and Classification
• Hands on coding.
• Pros and Cons
• Case study on ANN, MLP

Module 8: Tensorflow overview and Deep Learning Intro

• Tensorflow work flow demo
• Introduction to deep learning.

Module 1: Tableau Introduction

• Tableau Interface
• Dimensions and measures
• Filter shelf
• Distributing and publishing

Module 2: Connecting to Data Source

• Connecting to sources, Excel, Data bases, Api , Pdf
• Extracting and interpreting data.

Module 3: Visual Analytics

• Charts and plots with Super Store data

Module 4: Forecasting

• Forecasting time series data

Module 1: Understanding Business Case

• Components of Business Case.
• ROI calculation techniques.
• Scoping

Module 2: Writing Data Science Business Case

• Defining Business opportunity.
• Translating to Data Science problem.
• Creating project plan

Module 3: Benefits Analysis

• Demonstrating break even and benefits analysis with Data Science Solutions.
• IRR benefits analyis
• Discounted Cash Flow

Module 4: Starting project, Setting up Team and closing

• Initiating Project
• Setting up the Team
• Controling project delivery
• Closing project.

1. Introduction to Data Science

• What is Data Science?

• What is Machine Learning?

• What is Deep Learning?

• What is Artificial Intelligence?

• Data Analytics and its types

 

1. Introduction to R

• What is R?

• Why R?

• Installing R

• R environment

• How to get help in R

• R Studio Review

 

1. R Packages

• Data Types

• Variable Vectors

• Lists

• Environment Setup

• Array

• Matrix

• Data Frames

• Factors

• Loops

• Functions

• Packages

• In-Built Datasets

1. R Basics

2. Importing data

3. Manipulating data

4. Statistics Basics

5. Error metrics

6. Machine Learning

7. Supervised Learning

8. Unsupervised Learning

9. Machine Learning using R

Introduction to Deep Learning

• What is a neural network?
• Supervised Learning with Neural Networks - Python
• How Deep Learning is different from Machine Learning

Overview of Machine Learning Concepts

• What is Machine Learning?
• Supervised Machine Learning algorithms
• K-Nearest Neighbors (KNN) concept and application
• Naive Bayes concept and application
• Logistic Regression concept and application
• Classification Trees concept and application
• Unsupervised Machine Learning algorithms
• Clustering with K-means concept and application
• Hierarchial Clustering concept and application

TensorFlow Essentials

• Representing tensors
• Creating operators and excuting with sessions
• Introduction Jupyter notebook for TensorFlow coding
• TensorFlow variables
• Visualizing data using TensorBoard

ML Algorithm - Linear Regression in TensorFlow

• Regression problems
• Linear regression applications
• Regularization
• Available datasets
• Coding Linear Regression with TensorFlow - Case study

Deep Neural Networks in TensorFlow

• Basic Neural Nets
• Single Hidden Layer Model
• Multiple Hidden Layer Model

Convolutional Neural Networks

• Introduction to Convolutional Neural Networks
• Input Pipeline
• Introduction to RNN, LSTM, GRU

Reinforcement Learning in Tensorflow

• Concept of Reinforcement Learning
• Simple model applying Reinforcement Learning in TensorFlow

Hands on Deep Learning Application with TensorFlow

• Example Application - Case study
• Hands on building the Deep Learning application with TensorFlow

Introduction to TensorFlow

• Installing TensorFlow using Docker
• Installing Matplotlib
• Hello World applicatin with TensorFlow

Basic Statistics

• Basic Statistics and Exploratory Analysis
• Descriptive summary statistics with Numpy
• Summarize continous and categorical data
• Outlier analysis

Machine Learning Introduction

• Machine learning essentials
• Data representation and features
• Distance metrics
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Theano, Caffe, Torch, CGT, and TensorFlow

TensorFlow Essentials

• Representing tensors
• Creating operators and excuting with sessions
• Introduction Jupyter notebook for TensorFlow coding
• TensorFlow variables
• Visualizing data using TensorBoard

ML Algorithm - Linear Regression in TensorFlow

• Regression problems
• Linear regression applications
• Regularization
• Available datasets
• Coding Linear Regression with TensorFlow - Case study

ML Algorithm - Classification in TensorFlow

• Classification problems
• Using linear regression for classification
• Using logistic regression (including multi-dimensional input)
• Multiclass classifiers (such as softmax regression)
• Hands on Classificatin with TensorFlow

ML Algorithm - Clustering in TensorFlow

• Traversing files in TensorFlow
• K-means clustering
• Clustering using a self-organizing map

Simple Neural Networks in TensorFlow

• Introduction to Neural Networks
• Batch training
• Variational, denoising and stacked autoencoders

Reinforcement learning

• Concept of Reinforcement Learning
• Simple model applying Reinforcement Learning in TensorFlow

Convolutional and Recurrent Neural Networks

• Advantages and disadvantages of neural networks
• Convolutional neural networks
• The idea of contextual information
• Recurrent neural networks
• Real world predictive model - example

Case study - Stock Market Analsis with TensorFlow

• Case study - Stock Market Analsis
• Hands on Coding in TensorFlow

1. About Data Science with R Course

• Basic concepts of R Programming

• Stack, Merge and Strsplit

• Functions of R Calculator

• Assigning a value to variables and generating repeat and factor levels

• Performing sorting and analyze variance

• Creating charts, plots, and vectors

• ODBC Tables reading

• Database connectivity

• R language is one of the top languages that most of the companies are demanding.

• Learning Data Science with R opens new career opportunities for both beginners and professionals.

• It helps you achieve top programming jobs with the help of lab sessions, assignments and project work.

• Also, this training program helps you prepare for the R Certification exam.

After successful completion of the course “Data Science with R”, you should have,

• Gained a better knowledge of the workflow of data science with R language

• Understood the key concepts of data science

• Gained hands-on knowledge of R language

• In-depth knowledge of data manipulation, data visualization, advanced analytics and data mining.

• High demand for data scientists as a very less number of eligible candidates are available to be hired

• High salaries as per the demand for data scientists as supply is low

• Software Engineers and Data Analysts

• SAS developers who aspire to learn the open-source technology

• Business Intelligence professionals

• Candidates who want to start a career in Data Science

We have 6 solid reasons to say that our DataMites is one of the best institutes for data science in Bangalore and can strongly recommend you to join with us:

1. IABAC accredited

2. Elite Faculty & Mentors

3. Learning Approach

4. 10+ industry projects

5. 24*7 Cloud Lab for ONE year

Data Science involves the extraction of meaningful insights from large datasets, both structured and unstructured. It revolves around uncovering hidden patterns and transforming data into valuable information.

• Data Science enables the extraction of value from data.
• It improves the customer experience.
• Acquiring Data Science skills leads to a multitude of job prospects.
• Data Science professionals have the potential for higher salaries.
• There is a diverse array of job titles available in the field of Data Science.
• Data Science empowers individuals to actively contribute to decision-making in organizations.

Data Science courses are open to individuals of all backgrounds, whether beginners or experienced professionals. Engineers, marketers, software developers, and IT workers can enroll in part-time or external programs. Traditional Data Science courses typically require a high school education as a prerequisite.

The fee for Data Science courses varies depending on the level of training desired. Generally, classroom training for Data Science ranges from 1000 USD to 3000 USD, regardless of the chosen training provider.

Total course fee should be paid before 50% of the course completion. We also have EMI option tied up with bank. Check with coordinators.

Certified Data Scientist is delivered in both Classroom and Online mode. Classroom is provided in selected cities in India such as Bangalore, Hyderabad.

No, Most of software are free and open source. The guidelines to setup software is a part of course.

Yes. The IABAC Exam fee is included in the course fee. No extra fee is charged.

All the online sessions are recorded and shared so you can revise the missed session. For Classroom, speak to the coordinator to join the session in another batch.

We have a dedicated PAT (Placement Assistance team) to provide 100% support in finding your dream job.

Yes, you do get job support throughout the course training. We have a special team dedicated to placement assistance, called Placement Assistance Team (PAT). We help you build your proper resume, share and skill you with top interview questions and answers in the particular technology. Also, we discuss real-world projects using specific technology that you are into.

The different payment modes that are accepted by us:

1. Online payments 

2. Debit Card

 3. Credit Card

As per the latest reports from Glassdoor, the average salary of a Data Science with R professional earns Rs.62,02,144/- in Bangalore. Anyhow, the salary of an individual varies based on city, industry and total years of experience.

Data Science is ruling many domains around the world. Companies are paying high wages for data science professionals. Organizations are looking out for different types of job roles in data science like:

• Data Scientist

• Data Analyst

• Business Analytics Expert

• Support Engineer

• Statistical Programmer Specialist

• Research Analyst

• Business Analyst Consultant/ Manager