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

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

Data Science Basics

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

Python Fundamentals

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

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.

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.

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

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

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

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

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

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

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

Scientific Computing through Scipy

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

Python Integration with Spark

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

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

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

Restricted Boltzmann Machine and Autoencoders

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

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

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

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

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

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

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