Event Board
ABNiCO Academy offers All Courses 100% LIVE and Instructor Based

Python for Data Science

Python for Data Science

Sign Up For This Course

Upcoming Batches

September 22, 2021
Tools for Data science- numpy, pandas, matplotlib, seaborn
100% Live
5+ Projects on Live as well as widely used Dataset
Live AQ Session

Course Description

Python for Data Science
Python for Data science is an excellent course to explore the effective libraries like, pandas, numpy, matplotlib, seaborn and others which are dominantly used in data science arena. Nowadays python language becomes an indispensable tool to analyze the huge volumn of data and interpret the outcome in an organized manner. In oder to perform effective analysis using python, there are various assisting tools like, pandas, numpy, which helps us to manipulate those data in a easiest way. Due to simple using methodology and user-friendly nature, such tools are in high demand for every professionals in this domain. However, No prior programming experience is required to enroll into this course.

What We Do
We follow Step-by-step approach to write a programming from the scratch. Initally, we will be familier with preliminary syntax-semantics of python. Then we will introduce data science tools like, pandas, numpy etc with relevant dataset to illustrate the functionality of each modles separately. In the entire session we are going to bisects 5+ suitable dataset and all the analysis are categorized based on difficulty level This course includes Projects for gaining Hands-on experience of such effective tools. At the same time this Course will conclude with a Test followed by awarding a Certificate.

Dataset Domain:
Course Details

Classes

Every Wednesday, Saturday

From 6:30 pm

to 8:30 pm

Fees

INR. 3999/- Only

( Payable Once )

Duration

18 Hours

(2 Hours per Session)

python programming course
spoken english classes online

Sign up for this course

Sign up Now
Key Course Takeaways
1
Powerful Libraries like, numpy, matplotlib, seaborn, pandas will be at your fingertips
2
Industry oriented Dataset will be demonstrated though out the entire sessions.
3
Assignments given at the end of of each session for practice.
4
Hands-on experience are more prioritize rather than Theory elaboration
5
All the sessions are Interactive among Instructor and Learner and not designed like conventional monotonous Video Based courses.
6
This Course is designed for all students who are inexperience in coding.
Python for Data Science Course Content
Module 1: Introduction to Python
Fundamental of Python
  • Why Python and Use of Python in different domains such as Data science, Cyber security, Machine Learning, Various discipline of Engineering, Food Technology, Finance & Controlling, Management etc.
  • Introduction of ‘Python code writing’ Environment and different IDEs.
Python Setup
  • Installation of Anaconda in windows and LINUX.
  • Activating Jupyter Notebook and spider.
  • First Programming on Python (Hello World).
Module 2: Introduction of Numpy Library
  • Introduction of Array in numpy, Creation of Array (1D,2D,3-D and Boolean) using Numpy.
  • Perform different Operation on Array and fundamentals of different Data Types for Array
  • Implementation of zeros(), ones(), arange() and empty().
  • Scaler and Vector Operation on Array in numpy.
  • Indexing of Array
    • Boolean Indexing.
    • Fancy Indexing
  • Slicing of Array
  • Conditional If-else statement on Numpy array.
  • Use of Loop on Numpy array.
  • Use of Conditional Logic and Loop on Array Operations
  • Universal Functions (ufunc) or Universal Methods in numpy array.
  • Familiar with available Build-in ufunctions such as – Arithmetic Uniary ufunc, Arithmetic Binary ufunc, Statistical ufunc, Boolean ufunc in numpy.
  • How to Handle File with Numpy.
Module 3: Numpy Project on Live Dataset (Collected from Live Questionnaires across India) to analyze the dataset.
Module 4: Data Visualization using python, an introduction to matplotlib and seaborn Library
  • Introducing matplotlib and seaborn libraries in python.
  • Using Matplotlib Library Draw Line Chart, Bar Chart, Pie Chart, Histogram, Scatter Plot, Box Plot on different Dataset.
  • Using Seaborn Lbrary implement distplot(), Seaborn Jointplot (), Seaborn Kernel Distribution, Seaborn Heatmap.
Module 5: Introduction of pandas Library and understanding of Dataframe
  • Introduction to the Different Pandas data structure like Series and Dataframe.
  • How to create Series, Access Series elements and different Operation on Series elements.
  • Let us Dataframe in pandas.
  • Import our first CSV file using import and read_csv () method and Inspection of datafile.
  • Key features of Jupyter Notebook for pandas library.
  • Data selection from CSV File-
    • All Rows all Columns
    • All Rows selected Columns.
  • Use of loc() and iloc() functions in pandas.
  • iloc()- for Row selection (Used for Rows Selection).
    • Selected Rows and selected Columns.
    • Selected Rows and Selected column using loc().
  • How to Index and Re-index a loaded CSV file.
Module 6: Condition Based Data Filtering in pandas Library
  • Hands-on Filtering Dataframe using one condition.
  • Hands-on Different Operator and Filtering Dataframe using Multiple conditions
  • Removing Elements from Dataframe
    • Removing Columns
    • Removing Complete Rows
  • How to Add New Columns in Dataset (Adding Scaler Values, Create a new column).
  • Creating a New Dataframe from scratch.
Module 7: Joining and Merging using pandas syntax
  • Use of append() and concat()
  • Implementation of Outer Join using merge()
  • How to inner Join Dataframe.
  • Left Join & Right Join of Dataframe.
  • Joining on Different column name.
  • Group by on Dataframe.
Module 8: Dataframe Manipulation (BONUS Module)
  • Statistical functions on DataFrame.
  • String Operations on pandas.
Module 9: Dataset Pre-processing using pandas (Useful for Readiness of dataset for Build a Model using Machine Learning Classifiers)
Section-A (Data Cleansing)
  • Anomalies need to be taken care to handle Data Cleansing, but not limited to-
    • Removing inconsistent data from the dataset.
    • Dealing Missing Values and Removing Missing Values from the dataset.
    • Dealing Duplicate Values and Removing Duplicated Values.
    • Deal with Outliner and how to handle dataset outliners.
Section-B (Feature Extraction)
Module 10: Project on Pandas

We will perform the Data Analysis on the IPL Auction Dataset or any other suitable Dataset using pandas library.

data analysis using python

python programming course

error: