Data Science with Python

Overview Step into the exciting world of data science with our detailed Data Science with Python course. This course teaches …

Data Science with Python

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Data Science with Python Overview

Step into the exciting world of data science with our detailed Data Science with Python course. This course teaches you key Python programming skills and advanced techniques for managing data with tools like Pandas and NumPy. Starting from the basics of setting up your environment to tackling complex data tasks, you’ll learn how to handle, process, and analyse data effectively. This makes you skilled in dealing with real-world data science challenges. Each part of the course is thoughtfully put together to give you deep knowledge and practical abilities important in today’s data-focused industries.

Learning Outcomes

  • Set up Python environment efficiently.
  • Master Python basics including data types and structures.
  • Perform operations using NumPy arrays.
  • Handle, analyse, and manipulate data with Pandas.
  • Manage missing data and sort data frames effectively.
  • Create visually appealing data visualisations with Matplotlib.
  • Apply advanced data handling techniques like merging, joining, and concatenation.
  • Read and write data from various file formats including CSV and JSON.
  • Use Pandas for complex data transformations like grouping and pivoting.
  • Develop skills in statistical data analysis and data presentation.

Who Is This Course For

This course is ideal for beginners who aspire to enter the field of data science and for professionals who aim to enhance their data handling capabilities. It also suits anyone interested in learning Python programming for data analysis, including students, data analysts, and budding data scientists.

Eligibility Requirements

Applicants should have a basic understanding of programming concepts. While prior experience with Python is advantageous, it is not mandatory as the course begins with the fundamentals, making it accessible to those new to the language or to programming in general.

Entry Requirements

  • Age Requirement: Applicants must be 16 or older, making the course accessible to both young learners and adults.
  • Academic Background: No prior qualifications required, open to all backgrounds.
  • Language Proficiency: A good understanding of English is essential, as all lessons are in English.
  • Numeracy Skills: Basic writing and numeracy skills are needed to follow the course content.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path

 Embarking on this course opens numerous career opportunities in sectors like finance, healthcare, retail, and technology. Mastery of Python and its libraries for data analysis is highly valued in roles such as data analyst, data scientist, and machine learning engineer, where you can leverage data to drive decisions and innovations.

Course Curriculum

Course Introduction and Table of Contents
Course Introduction and Table of Contents 00:09:00
Introduction to Python, Pandas and Numpy
Introduction to Python, Pandas and Numpy 00:07:00
System and Environment Setup
System and Environment Setup 00:08:00
Python Strings
Python Strings – Part 1 00:11:00
Python Strings – Part 2 00:09:00
Python Numbers and Operators
Python Numbers and Operators – Part 1 00:06:00
Python Numbers and Operators – Part 2 00:07:00
Python Lists
Python Lists – Part 1 00:05:00
Python Lists – Part 2 00:06:00
Python Lists – Part 3 00:05:00
Python Lists – Part 4 00:07:00
Python Lists – Part 5 00:07:00
Tuples in Python
Tuples in Python 00:06:00
Sets in Python
Sets in Python – Part 1 00:05:00
Sets in Python – Part 2 00:04:00
Python Dictionary
Python Dictionary – Part 1 00:07:00
Python Dictionary – Part 2 00:07:00
NumPy Library - Introduction
NumPy Library Intro – Part 1 00:05:00
NumPy Library Intro – Part 2 00:05:00
NumPy Library Intro – Part 3 00:06:00
NumPy Array Operations and Indexing
NumPy Array Operations and Indexing – Part 1 00:04:00
NumPy Array Operations and Indexing – Part 2 00:06:00
NumPy Multi-Dimensional Arrays
NumPy Multi-Dimensional Arrays – Part 1 00:07:00
NumPy Multi-Dimensional Arrays – Part 2 00:06:00
NumPy Multi-Dimensional Arrays – Part 3 00:05:00
Introduction to Pandas Series
Introduction to Pandas Series 00:08:00
Introduction to Pandas Dataframes
Introduction to Pandas Dataframes 00:07:00
Pandas Dataframe conversion and drop
Pandas Dataframe conversion and drop – Part 1 00:06:00
Pandas Dataframe conversion and drop – Part 2 00:06:00
Pandas Dataframe conversion and drop – Part 3 00:07:00
Pandas Dataframe summary and selection
Pandas Dataframe summary and selection – Part 1 00:06:00
Pandas Dataframe summary and selection – Part 2 00:06:00
Pandas Dataframe summary and selection – Part 3 00:07:00
Pandas Missing Data Management and Sorting
Pandas Missing Data Management and Sorting – Part 1 00:07:00
Pandas Missing Data Management and Sorting – Part 2 00:07:00
Pandas Hierarchical-Multi Indexing
Pandas Hierarchical-Multi Indexing 00:06:00
Pandas CSV File Read Write
Pandas CSV File Read Write – Part 1 00:05:00
Pandas CSV File Read Write – Part 2 00:07:00
Pandas JSON File Read Write
Pandas JSON File Read Write Operations 00:07:00
Pandas Concatenation Merging and Joining
Pandas Concatenation Merging and Joining – Part 1 00:05:00
Pandas Concatenation Merging and Joining – Part 2 00:04:00
Pandas Concatenation Merging and Joining – Part 3 00:04:00
Pandas Stacking and Pivoting
Pandas Stacking and Pivoting – Part 1 00:06:00
Pandas Stacking and Pivoting – Part 2 00:05:00
Pandas Duplicate Data Management
Pandas Duplicate Data Management 00:07:00
Pandas Mapping
Pandas Mapping 00:04:00
Pandas Grouping
Pandas Groupby 00:06:00
Pandas Aggregation
Pandas Aggregation 00:09:00
Pandas Binning or Bucketing
Pandas Binning or Bucketing 00:08:00
Pandas Re-index and Rename
Pandas Re-index and Rename – Part 1 00:04:00
Pandas Re-index and Rename – Part 2 00:05:00
Pandas Replace Values
Pandas Replace Values 00:05:00
Pandas Dataframe Metrics
Pandas Dataframe Metrics 00:07:00
Pandas Random Permutation
Pandas Random Permutation 00:08:00
Pandas Excel sheet Import
Pandas Excel sheet Import 00:07:00
Pandas Condition Selection and Lambda Function
Pandas Condition Selection and Lambda Function – Part 1 00:05:00
Pandas Condition Selection and Lambda Function – Part 2 00:05:00
Pandas Ranks Min Max
Pandas Ranks Min Max 00:06:00
Pandas Cross Tabulation
Pandas Cross Tabulation 00:07:00
Matplotlib Graphs and plots
Graphs and plots using Matplotlib – Part 1 00:06:00
Graphs and plots using Matplotlib – Part 2 00:02:00
Matplotlib Histograms
Matplotlib Histograms 00:03:00
Order Certificate
Order Certificate 00:00:00

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