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Data Science_A Practical Guide for Beginners
Rating: 5.0 out of 5(3 ratings)
1,006 students

Data Science_A Practical Guide for Beginners

Mastering the Fundamentals Through Real-World Applications and Hands-On Projects in Data Science
Created byDr Padmini Panneer Selvam
Last updated 4/2025
English
English [Auto],

What you'll learn

  • Students will learn the end-to-end workflow of data science, from data collection and exploration to analysis and visualization.
  • Students will gain proficiency in using Numpy and Pandas to manipulate, transform, and analyze datasets through basic operations and functions.
  • Students will learn how to clean, handle missing data, and prepare datasets for analysis using techniques like data transformation and summarization.
  • Students will learn to create and customize various types of visualizations (e.g., scatter, line, bar plots) to communicate data insights effectively.
  • Students will acquire techniques for efficiently merging, concatenating, and reshaping large datasets, ensuring smooth handling of complex data.

Course content

2 sections • 20 lectures • 3h 24m total length

  • Introduction to Data science14:13

    Explore data science as a practical six-step process—problem statement to deployment—combining statistics, machine learning, and domain expertise to extract insights and drive informed decisions.

  • Facets of data9:51

    Explore the facets of data, from structured and unstructured formats to graph-based, machine-generated, streaming, and audio, visual data, and learn how natural language processing enables entity recognition and sentiment analysis.

  • Lecture 4:Data Science Process9:41

    Follow the six-step data science process—from setting research goals and data retrieval to data preparation, exploration, modeling, presentation, and automation—to deliver data-driven insights.

  • Lecture 5 : Introduction to Numpy4:27
  • Lecture 6:Creating array, attributes and objects6:50

    Explore numpy arrays by creating 0d to 3d arrays, using arange and array constructors, and inspect ndim, size, shape, dtype, and itemsize.

  • Lecture 7:Array basic operations10:25

    Explore basic array operations in python using numpy, including arithmetic, logical, and comparison operations on arrays with np.add, np.subtract, np.multiply, np.divide, np.power, and np.mod, plus numpy logical and comparison operators.

  • Lecture 8 :ARRAYS JOIN,SPLIT,SEARCH SORT12:14

    Learn to manipulate arrays with numpy, including joining with concatenate, hstack, and vstack. Split arrays with split and array_split, search with where, and sort along specified axes.

  • Lecture 9:Array indexing,slicing and iterating12:40

    Explore array indexing, slicing, and iterating with numpy across 1D, 2D, and 3D arrays. Learn positive and negative indexing, exclusive stop, and for loops to access and extract data efficiently.

  • Lecture 10:copying arrays,Array shape manipulation8:24

    Learn how copying arrays preserves the original data and reshapes one-dimensional data into two-dimensional forms. Explore identity and I functions to create diagonal matrices with different dtypes and diagonal offsets.

  • Lecture 11:EXPLORING DATA USING SERIES AND DATA FRAME15:44

    Learn to explore data with pandas series and data frames, mastering indexing, vectorized operations, and missing value handling with isna and fillna, including forward and backward fill.

  • Introduction to Data Science
  • Introduction to Numpy
  • Basic attributes on Numpy
  • Creating attributes on arrays
  • basic operation on arrays
  • Creation of random values

Requirements

  • Learners should have a basic understanding of mathematics and statistics, familiarity with programming fundamentals (preferably in Python), and a keen interest in data-driven problem solving.

Description

Course Description:

This course is designed to introduce beginners to the exciting and rapidly growing field of data science. Students will gain foundational knowledge of the data science process, including data collection, exploration, cleaning, and visualization. Through hands-on practice with essential Python libraries such as NumPy, Pandas, and Matplotlib, learners will develop the skills to manipulate arrays, work with large datasets, and draw meaningful insights from data. They will learn the importance of proper data handling, including techniques for merging datasets, cleaning missing values, transforming data, and preparing it for analysis. Visualization techniques will be explored using a variety of plot types to effectively communicate data-driven insights.

Whether you’re looking to pursue a career in data science or simply want to enhance your analytical skills, this course equips you with practical tools and experience to confidently work with real-world data.

Learning Outcomes:
By the end of this course, students will be able to:

  • Understand the data science workflow and its key components.

  • Perform data manipulation using NumPy and Pandas.

  • Clean, wrangle, and prepare large datasets.

  • Visualize data effectively using Matplotlib.

  • Apply data science techniques to real-world datasets.

  • Communicate insights clearly through analysis and plots .Thus this course would enable the students to meet the bridging between academia and industry needs.



Who this course is for:

  • This course is ideal for students, aspiring data scientists, working professionals, and anyone with a curiosity for data and a desire to build practical skills in data science from the ground up.

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