Data Science

Data science is a multidisciplinary field. It allows us to understand the world based on data. Data informs on important underlying concepts behind every problem that needs to be solved.

In this course, you will learn about methods used in the industry to analysis data and turn it into information. Data is the necessity of industries and therefore,

Data science has created a vast impact on all the applications. Several industries like banking, transport, e-commerce, healthcare and many more are using data science to better their products.

About This Course!

Data Science is a vast field and therefore, its applications are also enormous and diverse. Industries need data to move forward and therefore, it is an essential aspect of all the industries today.
Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. It encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.

This course can be taken by anyone who wants to understand how data continues to evolve as one of the most promising and in-demand career paths . It involves the use of analytical methods using industry-standard libraries like NumPy, pandas, Matplotlib and many others.
What you’ll learn
● Data wrangling and cleaning
● Working with statistical libraries in programming
● Data presentation with tools like Matplotlib
● Building data science models
● Deployment of analytics models.

What you’ll learn!

01

Python Crash Course
  • Working with python programming language.
  • Work with python variables, python data structures, modules
  • Work with python functions, conditional statements & loops,

Ex: Simple patterns, RPS Game, Guessing game, Contact book etc.

02

Mathematics, Statistics and Python Modules
  • Python modules required for data science will be taught .
  • Mathematics basics like probability, expected values, permutations and combinations
  • Working on matplots so performing examples on how to plot them
  • Statistics basics, models and applications.

03

Beginner Data Science
  • Working with NumPy, Expressing conditional logic as Array Operations
  • Working with Pandas,Mathematical and Statistical methods
  • Data Processing and Manipulation
  • Data Wrangling: Join, Combine, Reshape
  • Plotting and Visualization

04

Intermediary Data Science
  • Data cleaning and preparation
  • String manipulation
  • Data transformations
  • Combining and merging data sets
  • Data wrangling and working on datasets to apply all the learned concepts.

05

Advanced Data Science
  • Data Aggregation and group operations
  • Advanced statistical modules , Matplotlib API
  • Conduct data harvesting and exploratory analysis

06

Certification Project
  • Apply data manipulations, sorting out the data , analysing the data and cleaning the data in the given data set. And plotting all the features that can be formed in the given data set using pandas , seaborn and Matplotlib.
  • The students would have to complete the project on their own, but any kind of help and doubt clearance would be given from the instructor’s side.

Ex: Drawing pie charts, histogram, Create a Chat box etc

Student Projects

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