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Data Science Basics and Practical Approaches
Rating: 4.7 out of 5(43 ratings)
639 students

Data Science Basics and Practical Approaches

Introduction to Data Science, Numpy and Pandas, Data Wrangling, Data Cleaning and Preparation, Visualization
Created byDr. Poornima Selvaraj
Last updated 4/2025
English
English [Auto],

What you'll learn

  • Understand the basics of data
  • Learn the Pandas library to analyze data frames
  • Utilize different methods of data acquisition and data cleaning
  • Explore the visualization tools for different kinds of input data formats
  • Apply supervised and unsupervised learning to learn the hidden patterns from the data and predict the output

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Course content

3 sections • 21 lectures • 7h 21m total length

  • Introduction to Data Science10:05

    Explore data science basics and practical approaches, analyzing big data to extract insights from structured and unstructured data, including machine and human generated data, velocity, volume, and variety.

  • Facets of Data9:28

    Explore the facets of data across six categories: structured, unstructured, natural language, machine generated, graph-based data, and audio, image, and video, and their storage and analysis implications.

  • Data Analysis and Benefits14:27

    Explore data analysis concepts, distinguish quantitative and qualitative data, and apply descriptive, predictive, and prescriptive analytics to uncover insights, forecast outcomes, and drive business decisions.

  • Data Science Process25:52

    Learn the data science process by defining the problem, preparing and exploring data, selecting features, training and evaluating models, deploying and maintaining them, and extracting knowledge.

  • Introduction to Numpy and Array12:36

    Explore numpy, the numeric python library for scientific computing, focusing on arrays (ndarray), indexing and operations, data I/O, and linear algebra and Fourier transforms, plus interconnections with C/C++ through extensions.

  • Basic Array Operations26:19

    Create and manipulate arrays with numpy, including one-dimensional and two-dimensional arrays, joining, splitting, searching, and sorting with concatenate, vstack, hstack, array_split, vsplit, and argsort.

  • Advanced Array Operations39:12

    Learn NumPy indexing and slicing for 1D and multi-dimensional arrays, including negative indices, element modification, and reshaping; plus identity matrices with identity and eye.

  • Introduction to Pandas32:05

    Explore how pandas builds on NumPy with series and data frames, enabling flexible indexing, missing value handling with NaN, and powerful operations on tabular data.

  • Functions in Pandas44:13

    Explore advanced pandas functions—reindexing, sorting, drops, loc/iloc selections, arithmetic, and rank and describe operations—covering missing values with nan and forward fill for aligned data.

  • Data Acquisition26:53

    Explore data acquisition across disk, database, and web API sources, and practice reading and writing data with pandas, while managing indexing, type inference, time series, and missing values.

Requirements

  • No programming experience needed, basics of computers are needed
  • Basic machine learning concepts will help to understand much better

Description

This course offers a comprehensive introduction to the fundamentals of data science, focusing on both foundational concepts and practical applications. Designed for beginners, it combines theoretical insights with hands-on techniques to empower participants to analyze and interpret data effectively.

Students will learn core concepts such as data wrangling, statistical analysis, data visualization, and machine learning. The course emphasizes practical approaches to problem-solving using industry-standard tools like Python, along with libraries such as Pandas and Scikit-learn.

Real-world case studies will enable participants to build portfolios while exploring diverse domains like business, healthcare, and social sciences. By the end of the course, students will have the confidence to approach data-driven challenges and apply data science techniques to generate actionable insights.

Learners can Understand the key concepts of data science and its role in decision-making. Perform data cleaning, transformation, and analysis using programming tools. Develop and interpret data visualizations to communicate findings effectively. Apart from that, learners can apply basic machine learning algorithms to solve practical problems, Work with datasets from various domains in real-world case studies.

Beginners curious about data science, Professionals looking to add data analysis skills to their toolkit and majorly Students and individuals aspiring to pursue a career in data science can have a great learning experience from this course.

Who this course is for:

  • Python developers interested in learning data analysis

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