Data Science training in BTM, Bangalore
Data Science Training In BTM
Data science is one of the bestchosen options for students who know the technology. Data science is for the students who are seeking for python, R, Tableau etc. and willing to make it as their career option. Students can indulge in the IT sector with this and can also become a data scientist. In this new technical world it is necessary to take steps with it hence you can increase your capabilities and skills by taking high training program and become a data science expert.
Benefits Of Opting For Data Science Training In BTM
Check out the advantages of taking up a data science training in BTM:
1) The training makes the students evaluate and work on their skills.
2) A small number of students in the class help the teachers to have an eye on everyone so that no student is left behind.
3) A wellexperienced staff promotes selfconfidence which is quite beneficial for project handling in the future.
4) Facility of A+ class placements and guidance.
5) Daily availability of projects and Target to shape up your skills.
6) Helps in building selfconfidence, teamwork, leadership quality, Communication skills etc.
Data Minax Institute: Best Data Science Training Institute in BTM
Data Minax is a top data science training institute in BTM that provides an endless array of opportunities to all the students who are dreaming to achieve big in this field. The trainers train a student in such a way that the student can understand the industryspecific problems in a better way. The institute also provides a good atmosphere with deep analysis of the data science which makes this training institute a provider of the ultimate data science training in BTM. Get a pure blend of human expertise and computer knowledge to learn all the ins and outs in this field.
Why Choose Data Minax For Data Science Training In BTM
Data Minax uses a practical approach rather than theoretical in all its data science courses in BTM. There are a number of reasons to choose Data Minax over any other institute for your data science training in BTM:
1) The institute provides a full learning package so that you can make a grip upon the theoretical knowledge.
2) They have a small bunch of students in every batch so that they can get expertise to keep track of the performance of all of their students.
3) Computer lab facility to practice and groom their skills.
4) Experienced staff with a keen interest to make every student attain a better future.
5) Future internships programs are also available.
6) Different time slot availability so that one can choose a preferable time.
7) A good fee structure which is genuine and quite affordable.
Thus, owing to these reasons, Data Minax is a perfect platform for the students to make their data science skills sharpen. They train students who can serve well in the IT sector. A platform which enables its students a better data scientist and becomes an analytical expert in the field of data science.
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)