Data Science In Python Online Training

Linux_OS_Basics and Shell Scripting

Linux_Commands

File_System

vi editor

Advanced_Linux_Commands

System_Administration

Advanced_System_Administration

Grep

Shell_Scripting with examples(All types of loops)

Regular_Expressions

SED

AWK

MODULE 1: Introduction To Python - Data Science

Installation of Anaconda setup (Data Science Development Environment)

Installation of Pycharm

Working with Python List , List operation , Functions

Python Tuple , working and functions

Sets and Dictionary -operations and Working with them

Python More on Strings

Python Dates and Times

More on functions

Advanced Python Lambda

List Comprehensions

MODULE 2: Data Analysis

 Data Wandering

All about files Files

importing and exporting data with CSV files

XLRD module - working with xls .xlsx formats

Json data

XML data

Relational data Bases

Sql in python

Data quality Analysis

 DATA MANIPULATION - Cleaning - Munging - Cleansing Data with Python

strong>Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)

Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)

Python Built-in Functions (Text, numeric, date, utility functions)

Python User Defined Functions

Stripping out extraneous information

Normalising data

Formatting data

Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

 DATA VISUALIZATION

Introduction exploratory data analysis

Descriptive statistics, Frequency Tables and summarization

Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)

Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)

DATA ANALYSIS WITH PANDAS

The Series Data Structure

Querying a Series

The Data-Frame Data Structure

Data-Frame Indexing and Loading

Querying a Data-Frame

Indexing Data-frame

Understanding business problem

Selecting columns from Pandas Data Structures

Treating with missing values, outliers, NaN values

Creating new columns

Aggregate data ( use: groupby, merge, pivot, lambda)

Identifying unique values in data

Filter Data

Using basic functionality of Pandas API

MODULE 3: Mathamatics

 STASTISTICS

Basic Statistics - Measures of Central Tendencies and Variance

Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem

Inferential Statistics -Sampling - Concept of Hypothesis Testing

Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

Important modules for statistical methods: Numpy, Scipy, Pandas

PROBABILITY

Probability , Conditional Probability

Basic of Probability, Independent and Dependant events

Conditional Probability and Bayes Theorem

Continuous Probability Distributions

Mean, Median, Mode, Range

Determination of statistical techniques

Standard Deviation, Variance, Covariance, Correlation

outliners

Distribution of Data – Normal, Binomial, Gaussian

Different types of Data

Continuous , Categorical, Range

Testing of Hypothesis – which covers

Level of Significance (LOS), Level of Confidence, P-Value, T test, Z-test, ANOVA Test, CHI -Square Test

MODULE 4: Machine Learning

SUPERVISED LEARNING AND MODEL BUILDING

Process of Machine Learning

Model Building based on Data sets

Splitting Data: Training and Test sets

Regression Analysis (Linear, Multiple, Logistics Regression)

Classification concepts and Distance Functions

K-nn Algorithm concept and demonstration with data sets

Bayes Classification concept and demonstration with data sets

Decision Tree Algorithm concept and demonstration with data sets

Random Forests - Ensembling Techniques and Algorithms

2. UNSUPERVISED LEARNING AND MODEL BUILDING

Unsupervised Learning and Clustering Techniques

Centroid-based Clustering: K- Mean Algorithm concept and demonstration

Hierarchical Clustering concepts and Applications

Density-based Clustering: DBSCAN Algorithm concept and demonstration

 DIMENSION REDUCTION TECHNIQUES

Dimension Reduction Introduction

Why Dimension Reduction Required

LDA (Linear Discriminant Analysis) concept and applications

PCA (Principle Component Analysis) concept and applications

TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS

Introduction - Applications

Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition

Classification of Techniques(Pattern based - Pattern less)

vBasic Techniques - Averages, Smoothening

Advanced Techniques - AR Models, ARIMA

DATA SCIENCE PROJECTS WITH DATA SETS

Applying different algorithms to solve the business problems and bench mark the results