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.

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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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

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