#### Introduction to data science using python

• What is Data Science?
• Common Terms in Analytics
• Types of problems and business objectives in various industries
• Overview of analytics tools & their popularity
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Why Python for data science?

#### Python: Essentials

• Overview of Python- Starting with Python
• Introduction to installation of Python
• Introduction to Python IDE’s
• Understand Jupyter notebook
• Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
• Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
• List and Dictionary Comprehensions
• Basic Operations
• Simple plotting
• Control flow & conditional statements
• How to create class and modules and how to call them?

#### Data analysis - Visulization using python

• 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, Pandas etc)

#### Introduction to statistics

• Basic Statistics – Measures of Central Tendencies and Variance
• Building blocks – Probability Distributions – 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

#### Scientific distuributions used in python for data science

• Numpy, pandas, matplotlib, scikitlearn etc

#### Accessing/importing and exporting data using python modules

• Importing Data from various sources (Csv, txt, excel etc)
• Viewing Data objects – subsetting, methods
• Exporting Data to various formats
• Important python modules: Pandas

#### Data manupulation - Cleansing - Munging using python modules

• Cleansing Data with Python
• 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)
• Normalizing data
• Formatting data
• Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

#### Data exloration for modeling

• Need for structured exploratory data
• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
• Identify missing data
• Identify outliers data
• Visualize the data trends and patterns

#### Introduction to predictive modeling

• Concept of model in analytics and how it is used?
• Common terminology used in analytics & modeling process
• Popular modeling algorithms
• Different Phases of Predictive Modeling