Data Science

Learn about structured data processing and data visualization works with Python and its effective packages.

  • 50+ Hours
  • 2 Projects
  • 30 Assignments
  • Free Materials

Syllabus

Learn Data Science in Python Way: This course is for 3+ years experienced python programmers who wants to learn how to process data efficiently, prepare dataset for further analysis and visualizing data. It is all about using Python and its effective libraries such as numpy, pandas, matplotlib, sci-kit and scipy effectively.

Project Experience: During this course,  we will work on data mining, data visualization and deep in robust data science abilities over Social Network Analysis(Twitter and Facebook). Objective is to explore personal interest, profile viewers, most liked posts and tweets.

Introduction

  • Data Science
  • How Python fit for data science
  • Data type operations
  • Passing a function as variable
  • Embedding functions in another function
  • Passing a function as a parameter
  • Returning a function 
  • Altering a function behavior in another function (decorator)
  • Lambda, Map, Reduce, Filter
  • zip, izip

Processing Environments

  • IPython and Anaconda tool
  • Support libraries
  • Numpy
  • Data types of Numpy
  • Structured data processing
  • Pandas
  • Pre-processing and post-processing

Data Visualization

  • Matplotlib
  • Analyzing univariate data graphically
  • Grouping the data and using dot plots
  • Using scatter plots for multivariate data
  • Using heat maps
  • Performing summary statistics and plots
  • Using a box-and-whisker plot

Data Analysis

  • Explore and Wrangle
  • Imputing the data
  • Performing random sampling
  • Scaling the data
  • Standardizing the data
  • Performing tokenization
  • Removing stop words
  • Stemming the words
  • Performing word lemmatization
  • Representing the text as a bag of words
  • Calculating term frequencies and inverse document frequencies

Data Mining

  • Working with distance measures
  • Learning and using kernel methods
  • Clustering data using the k-means method
  • Learning vector quantization
  • Finding outliers in univariate data
  • Discovering outliers using the local outlier factor method

This course is project oriented.

Goal: 2 Applications Development

We will start with requirement analysis. Trainer will guide you to identify design components from the requirements. After code completion, we review your code and make it robust.

The first project is simple and its motive to complete the code with loops, functions, file operation and exception handling concepts. The second project requirement is advanced to the learner to gain project experience with real time requirements in which you need to use object oriented and database concepts additionally.

Daily assignments are to boost your problem solving ability. This will speed up you in writing code and bring more confidence in you. You will become a good python programmer if you could complete our simple & moderate assignments daily. We will train you how to write programs professionally.

 Goal: 30+ Assignments

 

You can schedule and attend Mock Interview in your convenient time. But this is not a part of course that you paid for.

Schedule

1. Python Interview Q&A
2. Project Summary
3. Areas to improve
4. Self-evaluation

We are arranging effective workshop monthly twice. You can reserve your seat for specific events listed. But this is not a part of course that you paid for.

Book your seat