Course Detail

• 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

• 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

• 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)

• 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)

• 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

• 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 , 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

• 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

• 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 Introduction
• Why Dimension Reduction Required
• LDA (Linear Discriminant Analysis) concept and applications
• PCA (Principle Component Analysis) concept and applications

• 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

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