Data Science Training Course in Bangalore

Mindmajix Technologies

Learn the basic as well as the advanced concepts like data analytics and business analytics of data science to become professional data scientists.

Online

30 Hours

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Frequency of Classes Weekdays, Weekends

Course overview

Data Science Training Course in Bangalore certification course is offered by MindMajix Technologies INC., which is created to provide the participants with knowledge of the core concepts of data science. Data Science Training Course in Bangalore online classes will teach them everything they need to know about data analytics, business analytics, Naive Bayes, data modeling, machine learning techniques, and much more.

Data Science Training Course in Bangalore online classes by MindMajix Technologies cover detailed video lectures along with lab training which includes topics such as R programming, predictive analytics, R data structures, data exploration, data visualization, R statistical concepts, cluster analysis, and forecasting. This training also provides enough opportunity to gain practical knowledge of the complex data science topics using real-world projects.

The highlights

Certificate of completion
Certification oriented curriculum
Lifetime self-paced video access
Flexible schedule
Project use cases
30 hours session
20 hours of labs
Quizzes & Mocks
Free demo on request
One-on-one doubt resolution
100% money-back guarantee
24/7 lifetime assistance

Program offerings

Certificate of completion
Certification oriented curriculum
Lifetime self-paced video access
Flexible schedule
Project use cases
30 hours session
Quizzes & mocks
Free demo on request
One-on-one doubt resolution
[oy

Course and certificate fees

certificate availability

Yes

certificate providing authority

Mindmajix Technologies

Who it is for

Data analyst Data scientist Software engineer Machine learning engineer Big data analytics engineer

What you will learn

Data science knowledge Knowledge of data visualization Knowledge of deep learning Machine learning Natural language processing Statistical skills R programming Knowledge of python Knowledge of apache spark Knowledge of big data Tableau knowledge Knowledge of mongodb Knowledge of excel

After completing the Data Science Training Course in Bangalore online certification, participants will gain a comprehensive understanding of data science and machine learning concepts. Participants will explore the various data operations including data manipulation, logistic regression, data visualization, data structures, and natural language processing. Participants will gain knowledge of artificial intelligence and deep learning as well as unsupervised learning to utilize their knowledge for data science operations. Participants will also learn about programs such as Python, Keras, Big Data Hadoop, TensorFlow API, Spark, Tableau, MongoDB, SAS, and MS Excel.

The syllabus

Module 1 - Data Science Basics

What is Data Science
Significance of Data Science in today’s world.
R Programming basics

Module 2 - Python Fundamentals

Python Introduction
Indentations in Python
Python data types and operators
Python Functions

Module 3 - Data Structures and Data Manipulation

Data Structures Overview
Identifying the Data Structures
Allocating values to the Data Structures
Data Manipulation Significance
Dplyr Package and performing different data manipulation operations.

Module 4 - Data visualization

Introduction to Data Visualisation
Various kinds of graphs, Graphics grammar
Ggplot2 package
Multivariant analysis by using geom_boxplot
Univariant analysis by using the histogram, barplot, multivariate distribution, and density plot.
Creating the bar plots for the categorical variables through geop_bar() and including the themes through the theme() layer.

Module 5 - Statistics

Statistics Importance
Statistics classification, Statistical terminology.
Data types, Probability types, measures of speed, and central tendency.
Covariance and Correlation, Binary and Normal distribution
Data Sampling, Confidence, and Significance levels.
Hypothesis Test and Parametric testing

Module 6 - Introduction to Machine Learning

Machine Learning Fundamentals
Supervised Learning, Classification in Supervised Learning
Linear Regression and mathematical concepts related to linear regression
Classification Algorithms, Ensemble Learning techniques

Module 7 - Logistic Regression

Logistic Regression Introduction
Logistic vs Linear Regression, Poisson Regression
Bivariate Logistic Regression, math related to logistic regression
Multivariate Logistic Regression, Building Logistic Models
False and true positive rate, Real-time applications of Logistic Regression

Module 8 - Random Forest and Decision Trees

Classification Techniques. Decision Tree Induction Algorithm
Implementation of Random Forest in R
Differences between classification tree and regression tree
Naive Bayes, SVM
Entropy, Gini Index, Information Gain

Module 9 - Unsupervised learning

Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
Unsupervised learning, Clustering algorithm, K-means clustering algorithm
K-means theoretical concepts, k-means process flow, and K-means implementation.
Implementing Historical Clustering in R
PCA(Principal Component Analysis) Implementation in R

Module 10 - Natural Language Processing

Natural language processing and Text mining basics
Significance and use-cases of text mining
NPL working with text mining, Language Toolkit(NLTK)
Text Mining: pre-processing, text-classification and cleaning

Module 11 - Mathematics for Data Science

Numpy Basics
Numpy Mathematical Functions
Probability Basics and Notation
Correlation and Regression
Joint Probabilities
Bayes Theorem
Conditional Probability, sum rule, and product rule

Module 12 - Scientific Computing through Scipy

Scipy Introduction and characteristics
Scipy sub-packages like Integrate, Cluster, Signal, Fftpack, and Bayes Theorem

Module 13 - Python Integration with Spark

Pyspark basics
Uses and Need of pyspark
Pyspark installation
Advantages of pyspark over MapReduce
Pyspark applications

Module 14 - Deep Learning and Artificial Intelligence

Machine Learning effect on Artificial Intelligence
Deep Learning Basics, Working of Deep Learning
Regression and Classification in the Supervised Learning
Association and Clustering in unsupervised learning
Basics of Artificial Intelligence and Neural Networks
Supervised Learning in Neural Networks, multi-layer network
Deep Neural Networks, Convolutional Neural Networks
Reinforcement Learning, dnn optimisation algorithms
Recurrent Neural Networks, Deep learning graphics processing unit
Deep Learning Applications, Time series modeling

Module 15 - Keras and TensorFlow API

Tensorflow Basics and Tensorflow open-source libraries
Deep Learning Models and Tensor Processing Unit(TPU)
Graph Visualisation, keras
Keras neural-network
Define and Composing multi-complex output models through Keras
Batch normalization, Functional and Sequential composition
Implementing Keras with tensorboard, customizing neural network training process
Implementing neural networks through TensorFlow API

Module 16 - Restricted Boltzmann Machine and Autoencoders

Basics of Autoencoders and rbm
Implementing RBM for the deep neural networks
Autoencoders features and applications

Module 17 - Big Data Hadoop and Spark

Big Data and Hadoop Basics
Hadoop Architecture, HDFS
MapReduce Framework and Pig
Hive and HBase
Basics of Scala and Functional Programming
Kafka basics, Kafka Architecture, Kafka cluster and Integrating Kafka with Flume
Introduction to Spark
Spark RDD Operations, writing spark programs.
Spark Transformations, Spark streaming introduction
Spark streaming Architecture, Spark Streaming Features
Structured streaming Architecture, Dstreams, and Spark Graphx

Module 18 - Tableau

Data Visualisation Basics
Data Visualisation Applications
Tableau Installation and Interface
Tableau Data Types, Data Preparation
Tableau Architecture
Getting Started with Tableau
Creating sets, Metadata and Data Blending.
Arranging visual and data analytics
Mapping, Expressions, and Calculations
Parameters and Tableau prep
Stories, Dashboards, and Filters
Graphs, charts
Integrating Tableau with Hadoop and R

Module 19 - MongoDB

MongoDB and NoSQL Basics
MongoDB Installation
Significance of NoSQL
CRUD Operations
Data Modeling and Management
Data Indexing and Administration
Data Aggregation Schema
MongoDB Security
Collaborating with Unstructured Data

Module 20 - SAS

SAS Basics
SAS Enterprise Guide
SAS functions and Operators
SAS Data Sets compilation and creation
SAS Procedures
SAS Graphs
SAS Macros
PROC SQL
Advance SAS

Module 21 - MS Excel

Entering Data
Logical Functions
Conditional Formatting
Validation, Excel formulas
Data sorting, Data Filtering, Pivot Tables
Creating charts, Charting techniques
File and Data security in excel
VBA macros, VBA IF condition, and VBA loops
VBA IF condition, For loop
VBA Debugging and Messaging

Instructors

Mr Abhishek
Instructor
Mindmajix Technologies

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