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Data Science using Python

data science using python

Learn Python for Data Science

Python for Data Science Online Training is a most preferred course by programmers. Python is used in tech companies around the world, from startups to big corporate companies. Data scientists use python extensively for data analysis and insight generation. At the same time, many companies choose it for its ease of use, extensibility. It even includes readability, openness, and therefore the completeness of python’s standard library.

Online Data Science Training With Python

The Python for Data Science Certification Course from Coursack offers hands-on introduction to this programming language. That’s essential for the aspiring data scientists entering the field. Python programming skills are totally in a high demand. And learning it can be like open doors to the endless opportunities in the fields of data science, machine learning, web development and more. The Python for Data Science certification course teaches you to master the concepts of Python programming.

1 to 1 Instructor led Training

Group Classes

Corporate Training

Student Discount

  • 35+ Hours of Training.

  • Class for Individual Students

  • Labs and Assignments

  • Class Recording is provided

  • 35+ Hours of Training.

  • Training is provided to group of students

  • Labs and Assignments

  • Class Recording is provided

Looking for corporate training ? Contact us for group discount 

  • 35+ Hours of Training.

  • Training is provided to group of students

  • Labs and Assignments

  • Class Recording is provided

Contact us

₹22,500

$380

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Through our Python for Data Science Online training, you’ll gain knowledge in machine learning, data analysis, data visualization, web scraping, & NLP. Upon course completion, you’ll master the essential tools of knowledge Science with Python.

Who is the targeted audience?

This Python for Data Science course is useful for analytics professionals willing to figure with Python, Software, and IT professionals curious about the field of analytics. Anyone with a genuine interest in Data Science can learn this training. The demand of professionals in Python for Data Science have surged and made this course well suited for the participants at all levels of experience.
Anil B
Anil B
2021-06-04
Great introduction to Python programming and everything is broken down to make it easy to understand. This is the first time taken course online and had a wonderful experience. Overall I recommend this course to anyone who is a beginner in programming or need to advance on their day to day work.
addanki susmitha
addanki susmitha
2021-06-03
One of the best training institute to make you ready for working on AWS. Venkat is having vast working experience in multiple product based MNCs in India and USA. With his experience, he knows what is required for the industry and provides personal coaching for each individual trainee. They also help in clearing certifications.
Aruna Padidam
Aruna Padidam
2021-05-31
Coursack is a wonderful institute.venkat prepares you for the finest details in the job market and gives you real time senarios. I would recommend coursack to anyone who is looking for a knowledgeable and committed team. Thanku venkat for a wonderful support. Glad to find you in the right time 🙂
Vijay Vijj
Vijay Vijj
2021-05-18
I strongly recommend Coursack. Venkat is very knowledgeable on the different aspects of training courses, his indepth ability of giving trainees solutions is commendable. They are different courses a trainee can take up. I took web development, Cyber security and DevOps classes. I am now working as a Developer in a very good company. Thanks for Coursack.
Phaneendra Jeenu
Phaneendra Jeenu
2021-05-18
It's a fun learning at coursack, all the courses were well designed such that you can upgrade yourself and the class exercises and real time projects helps you to gain good knowledge in core technical areas. I strongly recommend to all the aspirates, who want to upgrade in current latest trending technologies
santosh Kumar Voonna
santosh Kumar Voonna
2021-01-30
I know venkat for a long time and he is very genuine. That the most essential thing for anyone to run business. I strongly recommend him.
Ganesh
Ganesh
2021-01-06
Mr. Venkata made learning Python fun and interesting for my 9th grade daughter. Hoping that this will kindle and sustain a coding interest in her.
Malathi Varre
Malathi Varre
2021-01-06
The class was very useful and went over the topics very well. The teacher was able to help the students nicely when they had trouble and explained concepts clearly. My child was able to learn the basics of python easily thanks to this course.
Yashwant Bolishetti
Yashwant Bolishetti
2021-01-05
This class was very good.I learned a lot of things about Python coding and it was all very comprehensive.
Ayshkanta
Ayshkanta
2021-01-05
Pranav is a great tutor with vast knowledge his way of teaching was exceptional and I would refer him to all who wants their fundamentals to be clear.

1. Python Essentials

1
– Overview of Python- Starting with Python
2
– Introduction to installation of Python
3
– Introduction to Python Editors & IDE’s (Canopy, PyCharm, Jupyter, Rodeo, Ipython etc…)
4
– Understand Jupyter notebook & Customize Settings
5
– Concept of Packages/Libraries – Important packages (NumPy, SciPy, scikit-learn, Pandas, Matplotlib)
6
– Installing & loading Packages & Name Spaces
7
– Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
8
– List and Dictionary Comprehensions
9
– Variable & Value Labels – Date & Time Values
10
– Basic Operations – Mathematical – string – date
11
– Reading and writing data
12
– Simple plotting
13
– Control flow & conditional statements
14
– Debugging & Code profiling
15
– How to create class and modules and how to call them?

2. Scientific Distribution

1
– Numpy, scify, pandas, scikitlearn, statmodels, nltk

3. Accessing / Importing and Exporting Data using Python modules

1
– Importing Data from various sources (Csv, txt, excel, access etc.)
2
– Database Input (Connecting to database)
3
– Viewing Data objects – subsetting, methods
4
– Exporting Data to various formats
5
– Important python modules: Pandas, beautifulsoup

4. Data Manipulation

1
– Cleansing Data with Python
2
– Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting etc.)
3
– Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc.)
4
– Python Built-in Functions (Text, numeric, date, utility functions)
5
– Python User Defined Functions
6
– Stripping out extraneous information
7
– Normalizing data
8
– Formatting data
9
– Important Python modules for data manipulation (Pandas, NumPy, re, math, string, datetime)

5. Visualization using Python

1
– Introduction exploratory data analysis
2
– Descriptive statistics, Frequency Tables and summarization
3
– Univariate Analysis (Distribution of data & Graphical Analysis)
4
– Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
5
– Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density)
6
– Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats)

6. Introduction to Predictive Modeling

1
– Concept of model in analytics and how it is used?
2
– Common terminology used in analytics & modeling process
3
– Popular modeling algorithms
4
– Types of Business problems – Mapping of Techniques
5
– Different Phases of Predictive Modeling

7. Modeling on Linear Regression

1
– Introduction – Applications
2
– Assumptions of Linear Regression
3
– Building Linear Regression Model
4
– Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis)
5
– Assess the overall effectiveness of the model
6
– Validation of Models (Re running Vs. Scoring)
7
– Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers)
8
– Interpretation of Results – Business Validation – Implementation on new data

8. Modeling on Logistic Regression

1
– Introduction – Applications
2
– Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
3
– Building Logistic Regression Model (Binary Logistic Model)
4
– Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve)
5
– Validation of Logistic Regression Models (Re running Vs. Scoring)
6
– Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance)
7
– Interpretation of Results – Business Validation – Implementation on new data

9. Time Series Forecasting

1
– Introduction – Applications
2
– Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
3
– Classification of Techniques (Pattern based – Pattern less)
4
– Basic Techniques – Averages, Smoothening
5
– Advanced Techniques – AR Models, ARIMA
6
– Understanding Forecasting Accuracy – MAPE, MAD, MSE
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Enrolled: 225 students
Duration: 40 Hours
Lectures: 62
Level: Beginner

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Data Science using Python
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₹28,000 ₹22,500