Data Science Training

Learn Data Science from Karthik Trainings, where we commit to providing quality, practical and career-building Training. Join us to make a difference in the Data Science space!!

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Why Karthik Trainings for Data Science

Explore Data Science course from Karthik Trainings. We provide you with the best Data Science training in the market. We value your passion for making a difference in technology and providing you with the necessary environment to reach your goals. As a result, we have produced many Data Scientists in the market who are making remarkable differences in the technology in the Data Science space.

Our trainers are extremely talented and have the zeal to train the students in the Data Science space who are responsible for shaping your career as a Data Scientist. So come and join us in the journey of shaping dreams into reality. What we commit is what we deliver, which is our primary motto.

Data Science Key Features

Training from Industry Experts

100% Job Assistance

Career Guidance

Work Support

Live Projects with Real time scenarios

Timely Doubt Solving

Flexible Timing

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Data Science Syllabus

Overview and Evolution of Data Science

Understanding Data Science and its Evolution
Importance of Data Science in day to day life
Introduction to R Programming

Working with Python Fundamentals

Introduction to Python
Working on Indentations
Working with data types and operators
Understanding Python Functions

Introduction to Data Structures and Data Manipulation

An Overview on Data Structures
Working in Identifying the Data Structures
Allocation of values to the Data Structures
Manipulating Data and its importance
Dplyr Package and performing different data manipulation operations.

Working on Data visualization

Understanding Data Visualisation concept
Understanding Graphs and Graphics grammar
Working on Ggplot2 package
Multivariate analysis by using geom_boxplot
Univariate analysis with histogram, barplot, multivariate distribution, and density plot.
Working with the bar plots for the categorical variables through geop_bar() and including the themes through the theme() layer.

Working on Statistics

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

Machine Learning End-End Understanding

Understanding fundamentals of Machine Learning
Working with Supervised Learning, Classification in Supervised Learning
Working with Linear Regression and mathematical concepts related to linear regression
Working with Classification Algorithms, Ensemble Learning techniques

Working on Logistic Regression

Introduction to Logistic Regression
Understanding 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

Working on Random Forest and Decision Trees

Techniques.used in Decision Tree Induction Algorithm
Random Forest implementation in R
Classification tree vs Regression tree
Naive Bayes, SVM
Entropy, Gini Index, Information Gain

Understanding 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

Understanding and Working with Natural Language Processing

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

Introduction to Mathematics for Data Science

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

Scientific Computing through Scipy

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

Integrating python with Spark

Understanding Pyspark fundamentals
Pyspark usage
Installing Pyspark
Pyspark advantages comparing with MapReduce
Pyspark applications

Working on Deep Learning and Artificial Intelligence

Effect of Machine Learning on Artificial Intelligence
Basics and Working with Deep Learning
Regression and Classification in the Supervised Learning
Association and Clustering in unsupervised learning
Understanding 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

Working on Keras and TensorFlow API

Basics understanding on Tensorflow open-source libraries
Deep Learning Models and Tensor Processing Unit(TPU)
Graph Visualisation, keras
Keras neural-network
Defining 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

Working on Restricted Boltzmann Machine and Autoencoders

Autoencoders basics and rbm
Working RBM for the deep neural networks
Features and applications of Autoencoders

Working on Big Data Hadoop and Spark

Introduction to Big Data and Hadoop Basics
Working on Hadoop Architecture, HDFS
Working on MapReduce Framework and Pig
Working on 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

Working on Tableau

Basics of Data Visualisation
Working on Data Visualisation Applications
Working with Tableau Installation and Interface
Working with Tableau Data Types, Data Preparation
Understanding 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

Working on 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

Working on 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

Working with MS Excel

Understanding below
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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Frequently Asked Questions

Who Can Learn Data Science?

We know that DataScience will be our future and its demand in the market is exponentially increasing. Learning DataScience will be definitely a great choice to shape one’s career in the technology space.

Job Market in Data Science Space

Currently, in the market, there are a huge number of job openings in the DataScience space. Technically if we are good, getting a highly paid job is a simple task in this space.