What if I miss the online sessions?

The online sessions are recorded so you can watch them later and never miss a class.

What services are provided in job assistance?

Some services included in job assistance are resume building, mock interview training, discussing top interview questions, discussing real-world projects, etc.

Is this course suitable for business professionals?

Yes, this course is beneficial for business professionals.

Does this course provide classroom learning?

Yes, Data Science with R Programming training course includes a classroom learning option.

What payment modes are accepted for this programme?

You can pay for Data Science with R Programming programme through online payments, Debit cards and Credit cards.

Data Science with R Programming Course by DataMites: Fee, Duration, How to Apply

Data Science with R Programming Course

DataMites

Master data manipulation and visualisation with R programming by enrolling in the Data Science with R Programming course by DataMites.

Online

6 Months

₹ 73395 88000

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom +1 more	Mode of Delivery Video and Text Based

particular

details

                                    Medium of instructions
                                    English

Mode of learning Self study, Virtual Classroom

                                    Mode of Delivery
                                    Video and Text Based

Python for Data Science

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 - Numpy 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

Statistics for Data Science

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

Machine Learning Associate

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

Machine Learning Expert

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.

Tableau Foundation

Module 1 - Tableau Introduction

Tableau Interface
Dimensions and measures
Filter shelf
Distributing and publishing

Module 2 - Connecting to Data Source

Extracting and interpreting data
Connecting to sources, Excel, Databases, API, Pdf

Module 3 - Visual Analytics

Charts and plots with Super Store data

Module 4 - Forecasting

Forecasting time series data

Data Science Business Concepts

Module 1 - Understanding Business Case

Scoping
Components of Business Case.
ROI calculation techniques.

Module 2 - Writing Data Science Business Case

Creating project plan
Defining Business opportunity
Translating to Data Science problem

Module 3 - Benefits Analysis

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

Module 4 - Starting project, Setting up Team and closing

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

Data Science with R Training

Module 1 - Introduction to Data Science

What is Data Science?
Data Analytics and its types
What is Deep Learning?
What is Artificial Intelligence?
What is Machine Learning?

Module 2 - Introduction to R

What is R?
Why R?
Installing R
R environment
How to get help in R
R Studio Review

Module 3 - R Packages

Lists
Matrix
Array
Loops
Data Types
Packages
Functions
Factors
Variable Vectors
Environment Setup
Data Frames
In-Built Datasets

Module 4 - R Basics

Error metrics
Importing data
Manipulating data
Statistics Basics
Supervised Learning
Machine Learning
Machine Learning using R
Unsupervised Learning

Tensorflow Training

Module 1 - Introduction to Deep Learning

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

Module 2 - Overview of Machine Learning Concepts

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

Module 3 - TensorFlow Essentials

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

Module 4 - ML Algorithm - Linear Regression in TensorFlow

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

Module 5 - Deep Neural Networks in TensorFlow

Basic Neural Nets
Single Hidden Layer Model
Multiple Hidden Layer Model

Module 6 - Convolutional Neural Networks

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

Module 7 - Reinforcement Learning in Tensorflow

Simple model applying Reinforcement Learning in TensorFlow
Concept of Reinforcement Learning

Module 8 - Hands on Deep Learning Application with TensorFlow

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

Module 9 - Introduction to TensorFlow

Installing Matplotlib
Installing TensorFlow using Docker
Hello World application with TensorFlow

Module 10 - Basic Statistics

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

Module 11 - Machine Learning Introduction

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

Module 12 - TensorFlow Essentials

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

Module 13 - ML Algorithm - Linear Regression in TensorFlow

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

Module 14 - ML Algorithm - Classification in TensorFlow

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

Module 15 - ML Algorithm - Clustering in TensorFlow

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

Module 16 - Simple Neural Networks in TensorFlow

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

Module 17 - Reinforcement learning

Simple model applying Reinforcement Learning in TensorFlow
Concept of Reinforcement Learning

Module 18 - Convolutional and Recurrent Neural Networks

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

Module 19 - Case study: Stock Market Analsis with TensorFlow

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

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