Data Science Courses in Marathahalli, Bangalore
Data Science Courses in Marathahalli,bangalore
Data Science has become one of the most popular career options among young students. This branch of study consists of the learning and training about analytical tools like SAS, R, Python, Tableau and many more. It requires the knowledge about statistics, computer application techniques, mathematics and other incorporate studies like cluster analysis, data mining, machine learning etc.Learn Data Science Courses in Marathahalli.
Data Science course is necessary for all the individuals who want to pursue their career as a Data Scientist and Big Data specialist. It not only empowers the expertise of skills of the candidates but also prepares them to study the most technical components in the IT industry. There are a plethora of training courses available in order to sharpen your skill requirement and enhance your productivity level for the organizations.
Training Program:
Data Science Courses in Marathahalli offers excellent training programs and courses in order to enhance the efficiency and skill level of the candidates who want to become Data Scientist. These executives play a prominent role in increasing the productivity level and profit margins of the companies. They not only provide them the quantifiable information through IoT devices and data analytics but also facilitates in decisionmaking process.
Advantages of Data Science Courses in Marathahalli:
 Learn to overcome the mistakes in Data Science projects.
 Understand new areas of diversity and updates in the field of Big Data and Data Science.
 Enhance your skills by the team of top most experienced professionals.
 Work on live projects and assignments and understand the tactics of grabbing opportunities in Data Science management.
 Learn to handle and mitigate the risk factor in Data Science.
 Get placement assistance and guidance.
 Learn about the nontechnical skills like leadership, networking, communication, teamwork and many more.
 Get high paid salary packages in the topmost leading companies and IT industries like Cisco, Intel, Google, Dell etc.
Features of Data Science Training in Marathahalli:
 The training institutes offer fulltime course material in order to give theoretical knowledge to the individuals and improve their basics.
 There is a 24/7 lab facility for the students so that they can work on live projects and assignments for enhancing their analytical skills.
 These training included 60 hours of training classes along with the benefit of backup classes for those who want to learn from online sessions.
 Data Science Courses in Marathahalli involves a team of experienced professionals who have been teaching for more than 9 years.
 These institutes also assure you short period internship programs in order to improve your abilities and capabilities of Data Science and to provide you with a glimpse of working in a realtime work environment.
Data Science Courses in Marathahalli has become the fastest growing career path for the individuals. There has been a growing demand for Data Science specialists and Big Data analysts. Data Science has revolutionized the world of the IT sector and has turned out to be a building block for every business organization.
DataMinax ,Data science course in Marathahalli provides you the Real time course environment which leads to explore the knowledge & crack the interviews.
Course Features
 Lectures 130
 Quizzes 0
 Duration 1 hours
 Skill level All level
 Language English
 Students 24
 Assessments Yes

Part 1: Introduction to Data Science/Analytics
1. INTRODUCTION TO DATA MANAGEMENT
 i. Data Science – Brief
 Data Science Languages – Excel, SQL, Python & Tableau, R.,
 Data Preparation Techniques
 Sample Use Case
 2. STATISTICS AND EDA
 ii. Introduction to Statistics
 Population and Samples
 Descriptive vs Inferential Statistics
 Parameters vs Statistics
 Variable Classification
 Scale of Measurement
 Statistical Methods
 iii. Data Visualization
 Recognize difference between grouped and ungrouped data
 Construct Frequency Distribution
 Construction of bar diagram, column diagram, histogram,
 frequency polygon, pie chart scatter plot, bubble chart
 iv. Exploratory Data Analysis
 Measure of Shapes of Data
 Measure of central tendency for grouped & ungrouped Data
 Measures of Variability for Grouped and Ungrouped Data
 Various Outlier & Missing value treatments in data preparation
 Relationship between variables
 Correlation and causation.

Part 2: Introduction to R/Python
 Installing Python & Anaconda
 Basic Data Types
 Lists, Tuples & Dictionary
 Introduction to Numpy
 Numpy arrays and operations
 DataFrames & Series
 Group By with Pandas
 Merging, Joining, Concatenating & Interleaving
 Pandas Operations
 Plots with matplotlib and Seaborn
 Preprocessing with dataset
 EDA
 Case Study

Part 3: Distributions & Sampling

Part 4: Hypothesis Testing

Advanced Analytics
Part 5: Machine Learning
 1. Predictive Analysis – I
 i. Linear Regression
 Nature of Regression Analysis
 Meaning of the term linear
 Linearity in variables
 Linearity in parameters
 Method of Ordinary Least Square
 The classical linear regression model
 Standard errors of least square estimates
 The coefficient of determination r ^2
 R square and adjusted r square
 Goodness of fit
 The normality assumption
 Hypothesis testing
 Testing the overall significance of a multiple regression – F test
 Multicollinearity assumptions
 Autocorrelation
 Case study : Housing dataset
 ii. Logistic Regression
 Introduction
 Dichotomous dependent variable
 Odds and Odds Ratios
 The Logit Model
 Weight of Evidence and Information value
 Creation of Dummy Variable
 Parameter Estimates
 Goodness of fit, Concordance and discordance
 confusion matrix
 Sensitivity, Specificity , Information Gain
 ROC curve
 Case study : Prediction of Loan Dataset.
 iii. Clustering
 Cluster analysis intuition
 Types of Clustering
 KMeans Clustering
 Case Study
 iv. Principle Component Analysis
 2. Predictive Analysis – II
 i. MODEL SELECTION AND ADVANCED REGRESSION
 ii. DECISION TREES
 Random Forest regression
 Bagging and boosting
 Gradient Boosting
 XGBoost
 iii. NEURAL NETWORKS
 iv. TIME SERIES
 Stationary and Non Stationary Time Series
 Importing and cleaning
 Plotting
 Moving Averages
 Prediction (Holt Winters Method, MA, Exponential Smoothing)
 ARMA model
 Case Study

Part 6: Big Data – Overview

Part 7 : Deep Learning & NLP Overview
 1. NATURAL LANGUAGE PROCESSING
 BASICS OF TEXT PROCESSING
 LEXICAL PROCESSING
 SYNTAX AND SEMANTICS
 OTHER PROBLEMS IN TEXT ANALYTICS
 2. DEEP LEARNING & NEURAL NETWORKS
 INFORMATION FLOW IN A NEURAL NETWORK
 TRAINING A NEURAL NETWORK
 CONVOLUTIONAL NEURAL NETWORKS
 Use CNN’s to solve complex image classification problems
 RECURRENT NEURAL NETWORKS
 Study LSTMs and RNN’s applications in text analytics
 CREATING AND DEPLOYING NETWORKS USING TENSORFLOW AND KERAS
 Build and deploy your own deep neural networks on a website, learn to use tensorflow API and Keras

Part 8: Industry/Capstone Projects (Optional)