Data Science Online Training

DATA SCIENCE Data is the NEW Science. Big Data holds the Answers DATA SCIENCE Online Training Course
Average Rating: 4.7
Reviews: 76

Overview

QA Training Hub Hyderabad offers the most comprehensive online data science program. This training program will introduce you to main tools and ideas in the data scientist's toolbox. Students are equipped with data science skills that help them to become successful data analysts and data scientists.

This course focuses on ideas behind turning data into actionable knowledge and development of tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio. The objectives of our data science course are, to provide insight into the 'Roles' played by a Data Scientist, and analyze Big Data using R, Hadoop and Machine Learning.

By the end of the training program, the students will be able to understand the Data Analysis Life Cycle. If you are a business analyst or analytics manager, our online training program in data science helps you to understand machine learning techniques with implementation in Rlanguage.

Introduction to Python Programming

• Data
• Big Data
• Data Science Deep Dive
• Intro to R Programming
• R Programming Concepts
• Data Manipulation in R
• Data Import Techniques in R
• Exploratory Data Analysis (EDA) using R
• Introduction to Data Science
• Introduction to Python
• Basic Operations in Python
• Variable Assignment & Examples
• Functions: in-built functions, user defined functions & Examples
• Condition: if, if-else, nested if-else, else-if & Examples
• Data Structure's

• Introduction DS
• List Operations
• Different Data Types in a List,List in a List & with Examples
• Operations on a list: Slicing, Splicing, Sub-setting
• Conditions(True / False) on a List
• Applying Functions on a List
• Dictionary: Index, Value
• Operation on a Dictionary: Slicing, Splicing, Sub-setting
• Condition(True / False) on a Dictionary
• Applying functions on a Dictionary
• Numpy Array: Data Types in an Array, Dimensions of an Array & with Examples
• Operations on Array: Slicing, Splicing, Sub-setting
• Conditional(True / False) on an Array
• Loops: For, While with Examples
• Shorthand for For with Examples
• Conditions in shorthand for For & with Examples
• Basics of Statistics

• Introduction of Statistics & Plotting
• Introduction of Seabourn & Matplotlib
• Univariate Analysis on a Data
• Plot the Data - Histogram plot
• Find the distribution
• Find mean, median and mode of the Data
• Multiple Data with Same Mean with different sd, same mean & SD but different kurtosis: find mean, sd, plot & Examples
• Multiple data with different distributions
• Bootstrapping and sub-setting
• Making samples from the Data
• Making stratified samples - covered in bivariate analysis
• Find the mean of sample
• Central limit theorem
• Plotting
• Hypothesis testing + DOE
• Bivariate analysis
• Correlation
• Scatter plots
• Making stratified samples
• Categorical variables
• Class variable
• Use of Pandas
• File I/O
• Series: Data Types in series, Index
• Data Frame
• Series to Data Frame
• Re-indexing
• Operations on Data Frame: Slicing, Splicing (also Alternate), Sub-setting
• Pandas
• Stat operations on Data Frame
• Reading from different sources
• Missing data treatment
• Merge, join
• Options for look and feel of data frame
• Writing to file
• db operations
• Data Manipulation & Visualization

• Data Aggregation, Filtering and Transforming
• Lamda Functions
• Apply, Group-by , Map, Filter and Reduce
• Visualization
• Matplotlib, pyplot & Seaborn
• Scatter plot, histogram, density, heat-map, bar charts
• Linear Regression
• Regression - Introduction
• Linear Regression: Lasso, Ridge
• Variable Selection
• Forward & Backward Regression
• Logistic Regression

• Logistic Regression: Lasso, Ridge
• Naive Bayes
• Unsupervised Learning - Introduction
• Distance Concepts , Classification , k nearest
• Clustering, k means,Multidimensional Scaling
• PCA
• Random Forest
• Decision trees
• Cart C4.5
• Random Forest
• Boosted Trees
• SVM
• SVM - Introduction
• Hyper-plane
• Hyper-plane to segregate to classes
• Gamma
• Data Visualization in R
• Big Data and Hadoop Introduction
• Understand Hadoop Cluster Architecture
• Map Reduce Concepts
• Advanced Map Reduce Concepts
• Hadoop 2.0 & YARN
• PIG
• HIVE
• HBASE
• SQOOP
• Flume & Oozie
• Statistics + Machine Learning
• Python
• Machine Learning Using Python
• Projects

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