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Data Science, Machine Learning, Data Analysis, Python & R
Rating: 4.2 out of 5(3,409 ratings)
144,499 students

Data Science, Machine Learning, Data Analysis, Python & R

FREE Course on Data Science, Machine Learning, Data Analysis, Data Visualization using Python and R Programming
Created byDATAhill Solutions Srinivas Reddy
Last updated 1/2020
English

What you'll learn

  • What is Data Science
  • Artificial Intelligence vs Machine Learning vs Deep Learning
  • Data Analysis using Python and R
  • Data Visualization using Python and R
  • Data Loading using Python and R

Course content

1 section • 14 lectures • 8h 6m total length

  • Python Programming37:07

    Explore the Python programming language, from installation and basics to data types, data structures, libraries, and modules, and its role in data science and machine learning.

  • Numpy with Python34:41

    Learn to work with numpy arrays in python, covering array creation, shape, dimensions, dtypes, indexing, slicing, reshaping, and essential mathematical and statistical operations.

  • Pandas with Python43:03

    Explore pandas with Python to manipulate series and data frames, visualize and analyze data, and perform indexing, slicing, and loading from csv, Excel, and databases, handling missing values.

  • MatPlotLib with Python32:30

    Learn to visualize data with Matplotlib in Python, create line and bar plots, customize axes labels, colors, and markers, and combine multiple plots in a single figure.

  • Seaborn with Python31:46

    Learn seaborn with Python to visualize data, handle categorical data, and create multiple plots with statistical graphics, using datasets like iris and tips.

  • Python Machine Learning26:10

    Delve into data science and Python machine learning, comparing traditional programming with machine learning, and learn how to load data, train and evaluate models, and apply linear regression to problems.

  • Data Visualization using MatPlotLib & Seaborn32:45

    Learn to visualize data with Matplotlib and Seaborn in Python, creating line, bar, pie, and histogram graphs to report insights clearly for decision-makers.

  • Data Visualization using R Programming1:00:54

    Explore data visualization with R programming, building and customizing pie charts, bar charts, and 3D plots using high and low level plotting, libraries, and legends to translate statistics into visuals.

  • Data Visualization using Python and R32:48

    Explore data visualization with python and r using powerful libraries to create pie charts, bar charts, histograms, and line plots, with x and y axes, titles, and labels.

  • Data Analysis using NumPy and Pandas35:18

    Learn to analyze data with NumPy and pandas by manipulating arrays, indexing and slicing, and working with series and data frames, while loading data from csv, excel, and databases.

  • Data Analysis - How to Extract the Data43:40

    Load data from CSV, Excel, and databases into R, handle headers and delimiters, set the working directory, and perform basic inspection and subsetting.

  • Data Science30:42

    Combine statistics, machine learning, and data analysis to understand and analyze data through a full pipeline—from data collection to prediction and reporting.

  • Machine Learning25:06

    Explore what machine learning is, how it differs from traditional programming and statistics, and the types of learning, with data science context, data-driven modeling and real-world examples.

  • R Programming20:05

    Learn the R programming language, a free, open-source environment for statistical computing and graphics, with dynamic, interpreted scripting and a rich package ecosystem for data analysis and reporting.

Requirements

  • All graduates, Data analysts and business analysts

Description

Interested in the field of Data Science, Machine Learning, Data Analytics, Data Visualization? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Data Science. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

Who this course is for:

  • Beginner Python & R developers curious about Data Science

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