Data Science with Python

Jumpstart your career as a data scientist with this popular and action-packed course – Data Science with Python. Python is an open-source, high-level language and a valuable skill needed for a career in data science. This course aims to enable you with a thorough knowledge and concepts of python and provides a great approach object-oriented programming.

Content Provider

iNURTURE

Course Level

Intermediate

Mode Of Delivery

Self-Paced

Duration

40 hrs

Certification By

iNURTURE

Course Validity

Valid for 6 months post activation

Learning Resources

Videos Easy, Accessibility

Course Fee

This is a paid course.

Enroll Now

~~1,800/-~~ 1599/-

Description

Python is a high-level object-oriented programming language. This programming language consists of a wide range of applications from web development, scientific and mathematical computing. The syntax of this language is clean and has a short length of code. This programming language makes you think about the problem rather than focusing on the syntax. Python is used in creating many highly trafficked websites such as YouTube.

This Course

In this course you will get familiar with the basic concepts of python, statistics, regression etc. Further, you will learn about the advantage of Python offering programmers the advantage of using fewer lines of code to accomplish the tasks.

Learning Outcomes

Understand the core programming concepts of Python Programming Language

Know the Looping and condition statements in Python Programming Language

List the different options in Data Management in Python Programming Language

Appreciate the importance of data transformation and its need in Python Programming Language

Know elementary to advanced statistical methods in Python Programming environment

Curriculum

Module 1

Basic concepts in python

Calculations in python

Variable assignment

Function

Conditions

Data structures - List, Dictionaries, Numpy array, Slicing, Splicing, Subsetting, Functions, Conditions, Loops, Keys, Values, Datatypes.

Module 2

Statistics / Plotting - Seaborn vs Matplotlib

Univariate analysis - Import from csv

Plot histograms

Distribution

Mean

Data with same mean but different standard deviation

Data with same mean and standard deviation but different kurtosis,

Bootstrapping and subsetting - making samples, Mean of sample, Central limit theorem, Plotting, Hypothesis testing

Bivariate analysis- correlation, Scatter plots, Stratified samples, Categorical , Class variable

Module 3

Series – Datatypes

Index

Data frame - series to data frame

Reindex

Grouping

Pandas shortcuts

Reading from different sources

Missing data treatment

Merge

Join

Writing to file

Database operations.

Module 4

Regression- Data Aggregation, Filtering, Lamda functions, Map, Filter, Visualization, Matplotlib, Pyplot, Scatterplot, Histogram, Heatmaps

Regression – Linear, Lasso, Ridge, Variable selection, Forward & Backward regression, Polynomial regression

Module 5

Logistics regression

Naïve Bayes

Module 6

Unsupervised learning

Distance concepts

Classification

k-nearest

Clustering

k-means

Multidimensional scaling.

Module 7

Decision Trees

Random Forest

Boosted Trees

Gradient Boosting

Key Features

7 well-structured Learning Modules

12 Videos

Certification of Completion

Career Opportunities

After completing this course, you can seek career opportunities as:

Software Engineer

Data Science Analyst

Python Developer

Spark Programming

Application Engineer

Data Science

Certification

After completing this course and successfully passing the certification examination, the student will be awarded the “Data Science with Python” certification.

If a learner chooses not to take up the examination, they will still get a 'Participation Certificate'.

Who Should Attend

Engineering and IT students

Graduates with a programming background

Frequently Asked Questions

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