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Join the Best Institute for Data Science Course in Delhi (Uttam Nagar, Dwarka Mod, Janakpuri)

Data Science Using Python

About Instructor

Course Overview

Data science is an interdisciplinary field that extracts knowledge and insights from structured and unstructured data using scientific techniques and algorithms.

The best data science institute you will find is Uttam Nagar, Dwarka more & Janakpuri. In today’s environment, data has an impact on every element of the company. Data is now crucial in practically every business, from automobiles to healthcare, pharmaceuticals, and media to government and sports, and it is no longer unique to technological companies. Because of the exponential growth in data, qualified data scientists are in high demand to process and analyze data effectively and efficiently.

Course summary

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Course Introduction

Data science is an interdisciplinary field that uses scientific processes and various algorithms to extract knowledge and insights from data that may be e structured and unstructured.

 

Python has gathered a lot of interest recently e as a choice of language for data analysis/ science. Python is a free and open-source general-purpose programming language that is easy to learn. Python, due to itsversatility, is ideal for implementing the steps involved in data science processes. Python is being used for web development,data analysis, Artificial intelligence,and scientific computing.

 

The three best of most important Python libraries for data science and NumPy, Pandas, and Matplotlib. NumPy and Pandas are used for analyzing and exploring data. Matplotlib is a data visualization library used for making various types of graphs depicting the analysis.

Course objective

With the growth in the IT industry,there is a booming demand for skilled data scientists and Python has been involved as the most preferred programming language for the same.the course will focus on fundamental Python Programming techniques,reading and manipulating “.csv” files, And various libraries for data science.

 

Python has gathered a lot of interest recently e as a choice of language for data analysis/ science. Python is a free and open-source general-purpose programming language that is easy to learn. Python, due to itsversatility, is ideal for implementing the steps involved in data science processes. Python is being used for web development,data analysis, Artificial intelligence,and scientific computing.

 

The three best of most important Python libraries for data science and NumPy, Pandas, and Matplotlib. NumPy and Pandas are used for analyzing and exploring data. Matplotlib is a data visualization library used for making various types of graphs depicting the analysis.

After completing

After completing the module,the student will be able to:

  1. Take tabular data and clean it
  2. Manipulate the data
  3. Run basic inferential statistical analyses
  4. Perform data analysis
  5. Perform Visualization of analysis
  6. Built a Front-end GUI

Duration

Duration

120 Hours – (Theory: 48 hrs + Practical: 72 hrs)

Detailed Syllabus:

  1. Python Language, Structures, Programming Constructs

Review of Python Language, Data Types, Variables, Assignments, Immutable Variables, Strings, String Methods, Functions and Printing, Lists and Its operations, Tuples and Dictionaries programs, slicingof strings, Lists and tuples.

 

  1. Data Science and Analytics Concepts

What is data science and Analytics? The Data science process, Framing the problem, collecting, processing, cleaning and Munging Data, Exploratory data analysis, visualizing results.

 

  1. Introduction to NumPy library

NumPy: Array processing package, Array type, Array Slicing, Computation on NumPy Arrays – Universal Functions, Aggregations: Min Max etc., N-Dimensional Arrays, Broadcasting, Fancy indexing, sorting arrays, loading data in NumPy from various forms.

 

  1. Data Analysis Tool: Pandas

Introduction to the data Analysis Library Pandas, Pandas Objects – Series and Data Frame, Data Indexing and selection, Nan objects, manipulating data frames, Grouping Filtering, Slicing, Sorting, Ufunc, Combining Datasets – Merge and Join.

Query Data Frame structures for cleaning and processing, lambdas, Aggregation functions and applying user defined functions for manipulations.

 

  1. Statical Concepts and Functions

Statistics module, manipulating statistical data, calculating results, of statistical operations. Python probability Distribution, Functions like means, median, mode and standard deviation. Concept of Correlation and Regression.

 

  1. Matplotlib

Visualization with Matplotlib, Simple line plots, scatter plots, Density and Contour plots – visualizing functions, multiple subplots, Plotting histograms, bar charts, scatter graphs and line graphs.

 

  1. GUI – Tkinter

TKinter as Inbuilt Python module creating GUI applications in Python. Creating various widgets like button, canvas, label, entry, frame, check buttons etc. Geometry Managements: Pack, Grid, Place, organizing layouts and widgets, binding functions, mouse clicking events. Building the complete interface of a projects.

 

  1. Machine Learning: The Next Step

What is machine learning? Types of Machine Learning Algorithms, Training the data and introduction to various learning algorithms. Applications Machine Learning.

 

  1. Reference Books/Study Material
    1. Python for Data Analysis by O’Reilly
    2. Getting Started with Python Data Analysis
    3. Python Data Science Handbook: Essential Tools for Working with Data by O’Reilly
    4. Python for Data Science by Dummies

 

We offer comprehensive Data Science with Python training at the leading data science python institute in Delhi. Live projects and simulations are included in the full practical training provided by Data Science with a Python training facility in Delhi. Our students have been able to find jobs in a variety of MNCs thanks to this in-depth Data Science with Python course. The instructors are subject-matter experts from the corporate world who provide in-depth training in Data Science using Python in Delhi. Data Science with Python certification holders will have a wealth of work prospects in the business.

Learning interview skills is a must for anyone doing a Data Science with Python course in Delhi. We have sessions for personality development, spoken English, and presentation in addition to Data Science with Python training in Delhi.

Module: A10.1-R5-Data Science Using Python

Introduction:

Data science is an interdisciplinary field that uses scientific processes and various algorithms to extract knowledge and insights from data which may be structured and unstructured.

Python has gathered a lot of interest recently as a choice of language for data analysis/science. Python is a free and open source and a general- purpose programming language which is easy to learn. Python, due to its versatility, is ideal for implementing the steps involed in data analysis, intelligence, and science computing.

The three best and most important Python libraries for data science are NumPy, Pandas, and Matplotlib is a data visualization library used for marking various types of graphs depicting the analysis.

Objective:

With the growth in the IT indutry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language for the same. This course will focus on fundamental python programming techniques, reading and manipulating csv files, and the various libraries for data science.

  • After Completing the module, the student will be able to:
  • Take tabular data and clean it
  • Manipulating the data.
  • Run basic inferential statistical analysis.
  • Perform Data analysis
  • Perform visualization of analysis
  • Built a Front end GUI.

Duration:

120 Hours – (Theory:48 hrs + Practical:72 hrs)

ObjectiveLearning data Science is important as it help to explore data, analyze data, and draw out meaningful and insightful findings from your data. Learning Data Science provides an opportunity to recreate yourself. With Data science skills lots of high paying job opportunities in the field of academia, IT industries is seen.

Course Duration1 And Half Month

EligibilityKnowledge Of Python Required

Syllabus

Syllabus

Module Unit Duration(Theory) in Hours Duration(Theory) in Hours Learning Objectives (Learner will learn after completion of unit)
1. Python Language
, Structures, Programming
Contructs
6 9 -Write Programming in the Python Language.
-Extensively use conditional
2. Data Science Concept 2 3 1. The concept of Data Science
and Analytics and various
steps to achieve analysis.
3. Numpy 8 12 1. Scientific computing and
data analysis by understanding large
mulitidimensional arrays and matrices
2. Run Efficient operations on arrays
3. Work on high-level mathemetical
functions to operate on these arrays
4. Pandas 14 21 1. Data analysis after importing
data from various sources.
2.understand the Series and DataFrame as the central data Structures for data analysis.
3.Learn various functions, grouping for data analysis.
5. Statistical Concepts and Functions 6 9 1. The Statistical tool of python having ability to manipulate some Statistical data and calculate results of various Statistical operations.
2.Understand functions like mean, median, mode and standard deviatin.
3.Understand the concept of Correlation and Regression.
6. Matplotlib 6 9 1. Learn the python library used to create graphs and plots with just a few commands.
2.Understand pyplot and its feature of line styles, font properties, formatting axes etc.
3.Understand all aspects of the programmatical control of all the figures
7. GUI-Tkinter 4 6 1. The standard python interface to the Tk GUI tookit for creating quick and intuitive GUI.
2.The various widgets for input and theie event handling.
3.Integrating the data analysis and graphs in Tkinter.
8. Machine Learning 2 3 1. Overview of Machine Learning and its concept
Module Unit Written Marks (Max.)
1. Python Language, Structure, Programming Contructs 14
Data Science Concepts 6
Numpy 20
Pandas 24
Statistical Concepts and Functions 10
Matplotlib 10
GUI-Tkinter 12
Machine Learning-The Next Step 4
Total 100

Syllabus

Module Unit Duration(Theory) in Hours Duration(Theory) in Hours Learning Objectives (Learner will learn after completion of unit)
1. Python Language
, Structures, Programming
Contructs
6 9 -Write Programming in the Python Language.
-Extensively use conditional
2. Data Science Concept 2 3 1. The concept of Data Science
and Analytics and various
steps to achieve analysis.
3. Numpy 8 12 1. Scientific computing and
data analysis by understanding large
mulitidimensional arrays and matrices
2. Run Efficient operations on arrays
3. Work on high-level mathemetical
functions to operate on these arrays
4. Pandas 14 21 1. Data analysis after importing
data from various sources.
2.understand the Series and DataFrame as the central data Structures for data analysis.
3.Learn various functions, grouping for data analysis.
5. Statistical Concepts and Functions 6 9 1. The Statistical tool of python having ability to manipulate some Statistical data and calculate results of various Statistical operations.
2.Understand functions like mean, median, mode and standard deviatin.
3.Understand the concept of Correlation and Regression.
6. Matplotlib 6 9 1. Learn the python library used to create graphs and plots with just a few commands.
2.Understand pyplot and its feature of line styles, font properties, formatting axes etc.
3.Understand all aspects of the programmatical control of all the figures
7. GUI-Tkinter 4 6 1. The standard python interface to the Tk GUI tookit for creating quick and intuitive GUI.
2.The various widgets for input and theie event handling.
3.Integrating the data analysis and graphs in Tkinter.
8. Machine Learning 2 3 1. Overview of Machine Learning and its concept

Marks Distribution

Module Unit Written Marks (Max.)
1. Python Language, Structure, Programming Contructs 14
Data Science Concepts 6
Numpy 20
Pandas 24
Statistical Concepts and Functions 10
Matplotlib 10
GUI-Tkinter 12
Machine Learning-The Next Step 4
Total 100

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Start On April 28, 2016
Duration 2M
Level Beginner
Instution Adafa academia
Branch Oxford street
Seat avaiable 29
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