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The Python Bootcamp: Data Science, Analytics & Visualisation

12.5 hours on-demand video

$ 12.99

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Brief Introduction

Learn From Scratch!

Description

Reviews

"Perfect pace , clear and concise explanation on topics" - Deepak K

"This is my fourth Data Civilisation course I have undertaken, and I can assure you this will not be my last (if they continue to create the top end courses!)

From start to finish the course was delivered with such high standard and precision, building on every concept learnt as every section continued.

For someone (myself) who is looking to enter the data analysis sector, these guys have really built the confidence in me to do so and I am certain so many others who are in my position will benefit from this course..." - Imran Khan

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Add one of the most sought-after skills to your skill set!

This course will build your Python skills from scratch! The teaching methods used in this course will build on the foundation with you will gain to a high enough level where you will possess the ability to write Python code confidently and independently. As a result, you will be able to open multiple doors in the current job market!

If you want to learn Python operations, data analysis & analytics, data visualisation and the basics to data science, then this course is for you! All of these topics will be covered in Python 3!

This course contains more than 12 hours of lectures consisting of upwards spiral learning, so that you keep revisiting previous topics in the course. This will organically ensure that you are building your knowledge in all of the sections in this course, in addition to revising in the quizzes. There practical examples and applications are layered so that the complexity which you come across is easily digestible!

You will get lifetime access to this course and we will provide you with additional support if needed!

This course is broken down in the following manner:

(A) Python Operations:

Data Types
Numeric Operations
String Operations
Lists
Tuples
Dictionaries
Sets
'If' statement operations
'While' loop operations
'For' loop operations
List comprehensions
Creating your own functions
Object orientated programming (classes)

(B) Arrays (Numpy)

Structure of arrays (one and two dimensional)
Array operations
Applying filters to arrays
Analysing arrays

(C) Data Analytics (Pandas)

Importing data
Data frame operations
Filtering data
Sorting data
Bucketing data
Replacing data
Aggregations
Dealing with null values
Dealing with duplicate values
Appending data frames
Joins
Cumulative operations
Row number
Rankings

(D) Data Visualisation (Matplotlib)

Bar charts
Line charts
Pie charts

(E) Data Visualisation (Seaborn)

Scatter charts
Distribution plots

(F) Data Science

Anomaly testing
Linear regression
Multiple linear regression
K-nearest neighbours
Decision trees

This course is suitable for the following students:

Beginners who have no past coding or Python experience
SQL users who want to learn about how processes are carried out in Python
Intermediate users who have experience in Python that want to learn about Data Analysis/Analytics, Data Visualisation and an introduction to Data Science

Requirements

Requirements
None! Everything will be taught from scratch!
You will be using Jupyter Notebooks in this course, but all the installation steps will be covered in the course

Knowledge

How to navigate and utilise ‘Jupyter Notebooks’ for Python coding
Understanding the different data types in Python
How to carry out mathematical and string slicing operations on the respective data
The different series and data structures which are used in Python and how to run operations on them
How to use different Python statements to apply conditions to your code
Creating loops and iterations to drive Python operations
How to create your own Python functions
The basics to object orientated programming
The structure of arrays and how to operate on them by using the ‘Numpy’ module
How to carry out data analysis and analytics operations by using the ‘Pandas’ module
How to explore and understand data sets
How to apply operations on data sets to obtain useful information which provides meaningful insights
Understanding how to introduce relationships between multiple data sets
How to identify and resolve data quality issues
How to create visualisations in Python by using the ‘Matplotlib’ and ‘Seaborn’ modules
How to utilise statistical applications to identify potential anomaly instances in a data set
An introduction to Data Science applications
How to utilise linear regression and multiple linear regression models to make predictions
How to utilise the k-nearest neighbours’ model to make predictions
How to utilise the decision tree model to make predictions

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