Data Science Online Training

Data Science Online Training Course Content

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
• Gradient Boosting
• 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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