Data Science Training in Electronic City, Bangalore
Data Science Training in Electronic City
Among hundreds of courses provided in various colleges, Data Science holds a significant position in the minds of young students, making it very popular. When we talk about the things that one study in the course it is highly based on analytical tools like Python, Tableau, etc. their learning, and training. Data science demands for a basic knowledge of a few subjects such as statistics, techniques of computer application, mathematics along with a general study of incorporate subjects.
A student who wants to pursue a career as Data Scientist and Big Data specialist should necessarily have the Data Science Training in Electronic city. It boosts the ability and skills of the candidates and encourages them to study technical components in the IT industry considered as most essential. One can find a number of training courses in order to hone one’s skill as per their requirements and add to their productivity level as well as of the organizations.
Essential points of Data Science Training in the electronic city:
Data Minax is the best data science training institute in the electronic city and offers brilliant training courses to boost the efficiency and skill of the students who are willing to become a Data Scientist. If we look when these go to work in an organization they play key roles in improvising the productivity and profit margins of the companies. These students provide the company with significant information and assists in better decisionmaking of specific plans that might help in the long run.
Let’s discuss some essential points of data science training in electronic city.
 Improvement of basis is very important and everything alone cannot be done by training, therefore, fulltime course material is provided for theoretical
 The time duration of the training class is about 60 hours and one can take backup and also online classes are provided.
 The top data science training institute in electronic city has professional with teaching experiences of almost a decade.
 In order to increase your analytical ability and develop an understanding of work, internships are provided by the institute.
Advantages of Data Science Training in electronic city:
Through training one totally eliminates the chances of error in Data Science projects. Data science courses in the electronic city help you to develop an understanding of diverse things in the new areas and updates in the field. Check out some crucial benefits of taking this training:
 Boosting skills is bound to happen when the best professionals teach you.
 Grabbing opportunities in Data Science management can be very easy if one has gained live experience in it, as provided by the institute.
 The risk factor is important everywhere even in data science, you can learn best to eliminate it through experience.
 Improving skills like leadership, communication, teamwork are additional advantages.
 At the time of placement, one can get a job in reputed companies with high packages.
Conclusion:
Data Science has played a big role in innovating the IT sector and has become the strength of many organizations, therefore, growth among Data Science specialists and Big Data analysts can be witnessed.
Course Features
 Lectures 130
 Quizzes 0
 Duration 1 hours
 Skill level All level
 Language English
 Students 12
 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)