Python Data Analysis

Part 1

Introduction, Applications and Frameworks, Get Started with programming, Variables and Data Types, Operators and Expressions , Control Structure, Sequence Types, Dictionaries and Sets , List Comprehensions, Functions, Local, Non Local & Global Variables, Anonymous and Lambda Functions

Part 2

Environment Set Up , Anaconda, IPython Shell, IPython Notebooks, Pycharm, Spyder IDE, Run Python scripts, Loading packages, namespaces

Part 3

Numerical Analysis using NumPy, Introduction to NumPy, NumPy overview , Creating NumPy arrays , Doing math with arrays , Indexing and slicing , Records and dates , Downloading and parsing data files , Using Scipy

Part 4

Accessing and Preparing Data, Acquiring Data with Python, Loading from CSV files, Accessing SQL databases, Cleansing Data with Python, Stripping out extraneous information, Normalizing data, Formatting data, Debugging, Code profiling

Part 5

Data manipulation with Pandas, Pandas overview , DataFrames in pandas , Using multilevel indices, Series in pandas , Statistical analysis , Grouping, aggregating and applying, scipy.stats , Tabular Data Analysis with Pandas , Data Munging in Python using Pandas

Part 6

Advanced Analytics, SciPy and Scikit , Data Modelling , Machine Learning with scikit-learn, Estimator, predictor, transformer interfaces, Pre-processing data , Building a Predictive Model , Regression , Classification , Model selection , Logistic Regression , Decision Tree, Random Forest

Part 7

Visualization Tools , Overview , Mathplotlib , Numpy , Seaborn , Input: 2D, samples, and features , statistical graphics , Data Reporting , Extract datasets for specific reports (routine and adhoc) , Prepare reports on observed trends and patterns( Daily/weekly/monthly & quarterly , Develop graphs, reports, and presentations based on observation., Create management dashboards based on derived data collections.

Part 8

Web Scrapping & NLK , NLK, Scrapy.py, urllib , Pylib , Beautiful soup

Part 9

Project