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Career Track - Data Scientist

Data Science, Machine Learning, Deep Learning (Keras, TF) , Data Visualization with R & Python, R Programming



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£40 inc VAT (was £1,000)
Offer ends 30 June 2021
Study method
Online, self-paced
250 hours
Access to content
Lifetime access
No formal qualification
Additional info
  • Certificate of completion available and is included in the price

24 students purchased this course

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Data Scientists are a new breed of analytical data experts who have the technical skills to solve complex problems. The data scientist role also has academic origins. The Data scientist’s toolbox terms and technologies are commonly used by Data Scientist, Data visualization, Machine learning, Deep learning, Data preparation, Text analytics. Most Data Scientists have backgrounds as data analysts or statisticians, other come from non-technical fields such as business or economics.

In today’s technologically advanced world the field is gaining immense popularity across different sectors. As a result of large chunks of data created in every nook and corner.

The following courses are covered in the Data Scientist Career Track program:

  1. Data Science with Python
  2. Python Programming (basic to advanced)
  3. R Programming
  4. Machine Learning (basic to advanced)
  5. Machine Learning with Python
  6. Deep Learning Foundation
  7. Deep Learning with Keras

The candidate must have Data Science skills:

Fundamentals of Data Science - The first and foremost important skill you require is to understand the fundamentals of data science, machine learning, and artificial intelligence as a whole. Difference between machine learning and deep learning, common tools, and terminologies.

Programming knowledge - Python is a general-purpose programming language having multiple data science libraries along with rapid prototyping whereas R is a language for statistical analysis and visualization.

Data Manipulation and Analysis – Data wrangling make take up a lot of time but ultimately helps you in taking better data-driven decisions. Some of the data manipulation and wrangling generally applied is – missing value imputation, correcting data types, and transformation.

Data Science with Python- Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing, and analyzing data for various needs. Be it about making decisions for the business, forecasting weather, studying protein structures in biology, or designing a marketing campaign.

Python Programming (Basic to advanced) is a General Purpose object-oriented programming language, which means that it can model real-world entities. It is also dynamically-typed because it carries out type-checking at runtime. It does so to make sure that the type of construct matches what we expect it to be.

R Programming - R is a programming language and software environment for statistical analysis, graphics representation, and reporting. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.

Machine Learning (basic to advanced) -is an add-on to the Data Scientist skill set. ML is a subset of AI that contributes to the modeling of data. It uses algorithms like K-nearest neighbors, Random Forests, Naïve Bayes, and Regression Models.

Machine Learning with Python - Machine learning is a growing technology that enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Machine Learning is a subset of AI that is mainly concerned with the development of algorithms that allow a computer to learn from the data and past experiences on their own.

Deep Learning Foundation - Deep learning is also known as deep structured learning, is a subset of machine learning and refers to neural networks that have the ability to learn the input data increasingly abstract representations. Artificial Intelligence and Deep Learning are revolutionizing technology, business, services, and industry in a manner not seen before. This has been possible due to rapid progress and strides made in the computing and graphics processor technologies and the widespread use of the internet and mobile devices.

Deep Learning with Keras essentially means training an Artificial Neural Network (ANN) with a huge amount of data. In deep learning, the network learns by itself and thus requires humongous data for learning.

Keras, it is high-level neural networks API that runs on top of TensorFlow an end to end open source machine learning platform. Using Keras, easily define complex ANN architectures to experiment on your big data.

This course, Deep Learning with Keras will get up to speed with both the theory and practice of using keras to implement deep neural networks.

Software engineers who are curious about data science and about the Deep Learning Buzz and data scientists who are familiar with Machine learning and want to develop a strong foundational knowledge of deep learning.

Uplatz provides this Data Scientist course which covers all Modules – Data Science and Data Science with Python.

Course media


1. Introduction to Data Science

  • Introduction to Data Science

  • Python in Data Science

  • Why is Data Science so Important?

  • Application of Data Science

  • What will you learn in this course?

2. Introduction to Python Programming

  • What is Python Programming?

  • History of Python Programming

  • Features of Python Programming

  • Application of Python Programming

  • Setup of Python Programming

  • Getting started with the first Python program

3. Variables and Data Types

  • What is a variable?

  • Declaration of variable

  • Variable assignment

  • Data types in Python

  • Checking Data type

  • Data types Conversion

  • Python programs for Variables and Data types

4. Python Identifiers, Keywords, Reading Input, Output Formatting

  • What is an Identifier?

  • Keywords

  • Reading Input

  • Taking multiple inputs from user

  • Output Formatting

  • Python end parameter

5. Operators in Python

  • Operators and types of operators

- Arithmetic Operators

- Relational Operators

- Assignment Operators

- Logical Operators

- Membership Operators

- Identity Operators

- Bitwise Operators

  • Python programs for all types of operators

6. Decision Making

  • Introduction to Decision making

  • Types of decision making statements

  • Introduction, syntax, flowchart and programs for

    - if statement

    - if…else statement

    - nested if

  • elif statement

7. Loops

  • Introduction to Loops

  • Types of loops

    - for loop

    - while loop

    - nested loop

  • Loop Control Statements

  • Break, continue and pass statement

  • Python programs for all types of loops


    • Number Type Conversion
    • Random Number Functions
    • Trigonometric Functions
    • Mathematical Constants


    • Accessing Values in Strings
    • Updating Strings
    • String Special Operators
    • Built-in String Methods

8. Lists

  • Python Lists

  • Accessing Values in Lists

  • Updating Lists

  • Deleting List Elements

  • Basic List Operations

  • Built-in List Functions and Methods for list

9. Tuples and Dictionary

  • Python Tuple

  • Accessing, Deleting Tuple Elements

  • Basic Tuples Operations

  • Built-in Tuple Functions & methods

  • Difference between List and Tuple

  • Python Dictionary

  • Accessing, Updating, Deleting Dictionary Elements

  • Built-in Functions and Methods for Dictionary

10. Functions and Modules

  • What is a Function?

  • Defining a Function and Calling a Function

  • Ways to write a function

  • Types of functions

  • Anonymous Functions

  • Recursive function

  • What is a module?

  • Creating a module

  • import Statement

  • Locating modules


  • Printing to the Screen
  • Opening and Closing Files
  • The open Function
  • The file Object Attributes
  • The close() Method
  • Reading and Writing Files
  • The write() Method
  • The read() Method
  • More Operations on Files


  • What is Exception?
  • Handling an Exception
  • The except Clause with No Exceptions
  • The except Clause with Multiple Exceptions
  • The try-finally Clause
  • List of Standard Exception
  • Raising an Exception
  • Argument of an Exception


  • What is an Object?
  • What is a Class?
  • Creating a Class
  • Creating an object
  • Self in Python
  • __init__ method
  • Examples

14. Working with Files

  • Opening and Closing Files

  • The open Function

  • The file Object Attributes

  • The close() Method

  • Reading and Writing Files

  • More Operations on Files

15. Regular Expression

  • What is a Regular Expression?

  • Metacharacters

  • match() function

  • search() function

  • re.match() vs

  • findall() function

  • split() function

  • sub() function

16. Introduction to Python Data Science Libraries

  • Data Science Libraries

  • Libraries for Data Processing and Modeling

    - Pandas

    - Numpy

    - SciPy

    - Scikit-learn

  • Libraries for Data Visualization

    - Matplotlib

    - Seaborn

    - Plotly

17. Components of Python Ecosystem

  • Components of Python Ecosystem

  • Using Pre-packaged Python Distribution: Anaconda

  • Jupyter Notebook

18. Analysing Data using Numpy and Pandas

  • Analysing Data using Numpy & Pandas

  • What is numpy? Why use numpy?

  • Installation of numpy

  • Examples of numpy

  • What is ‘pandas’?

  • Key features of pandas

  • Python Pandas - Environment Setup

  • Pandas – Data Structure with example

  • Data Analysis using Pandas

19. Data Visualisation with Matplotlib

  • Data Visualisation with Matplotlib

    - What is Data Visualisation?

    - Introduction to Matplotlib

    - Installation of Matplotlib

  • Types of data visualization charts/plots

    - Line chart, Scatter plot

    - Bar chart, Histogram

    - Area Plot, Pie chart

    - Boxplot, Contour plot

20. Three-Dimensional Plotting with Matplotlib

  • Three-Dimensional Plotting with Matplotlib

    - 3D Line Plot

    - 3D Scatter Plot

    - 3D Contour Plot

    - 3D Surface Plot

21. Data Visualisation with Seaborn

  • Introduction to seaborn

  • Seaborn Functionalities

  • Installing seaborn

  • Different categories of plot in Seaborn

  • Exploring Seaborn Plots

22. Introduction to Statistical Analysis

  • What is Statistical Analysis?

  • Introduction to Math and Statistics for Data Science

  • Terminologies in Statistics – Statistics for Data Science

  • Categories in Statistics

  • Correlation

  • Mean, Median, and Mode

  • Quartile

23. Data Science Methodology (Part-1)

Module 1: From Problem to Approach

  • Business Understanding

  • Analytic Approach

Module 2: From Requirements to Collection

  • Data Requirements

  • Data Collection

Module 3: From Understanding to Preparation

  • Data Understanding

  • Data Preparation

24. Data Science Methodology (Part-2)

Module 4: From Modeling to Evaluation

  • Modeling

  • Evaluation

Module 5: From Deployment to Feedback

  • Deployment

  • Feedback


25. Introduction to Machine Learning and its Types

  • What is a Machine Learning?

  • Need for Machine Learning

  • Application of Machine Learning

  • Types of Machine Learning

    - Supervised learning

    - Unsupervised learning

    - Reinforcement learning

26. Regression Analysis

  • Regression Analysis

  • Linear Regression

  • Implementing Linear Regression

  • Multiple Linear Regression

  • Implementing Multiple Linear Regression

  • Polynomial Regression

  • Implementing Polynomial Regression

27. Classification

  • What is Classification?

  • Classification algorithms

  • Logistic Regression

  • Implementing Logistic Regression

  • Decision Tree

  • Implementing Decision Tree

  • Support Vector Machine (SVM)

  • Implementing SVM

28. Clustering

  • What is Clustering?

  • Clustering Algorithms

  • K-Means Clustering

  • How does K-Means Clustering work?

  • Implementing K-Means Clustering

  • Hierarchical Clustering

  • Agglomerative Hierarchical clustering

  • How does Agglomerative Hierarchical clustering Work?

  • Divisive Hierarchical Clustering

  • Implementation of Agglomerative Hierarchical Clustering

29. Association Rule Learning

  • Association Rule Learning

  • Apriori algorithm

  • Working of Apriori algorithm

  • Implementation of Apriori algorithm

Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make exact predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.

Skills required for a Data Scientist:

  • A solid understanding of both supervised and unsupervised machine learning methods
  • A strong understanding of statistics and the ability to evaluate statistical models
  • More advanced data-science-related programming skills in Python or R, and potentially familiarity with other tools like Apache Spark


  • Metacharacters
  • match() function

Who is this course for?

  • Anyone interested in Data Science
  • Anyone interested in a Data Scientist career
  • Software developers or programmers
  • People who want to work as data scientists
  • People curious about data science
  • You should take this course if you want to become a Data Scientist or if you want to learn about the field
  • This course is for you if you want a great career


Passion and Determination to achieve your goals!!!

Career path

  • Data Scientist
  • Data Scientist senior to Lead
  • Senior Data Science Engineer
  • Data Science Instructor
  • Data Analyst
  • Junior Data Scientist
  • Data Scientist - Upstream
  • Python with Data Science - Trainer
  • Lead Data Scientist
  • Software Engineer (Data Scientist)
  • Data Scientist - Machine Learning
  • Principal Data Scientist
  • Associate Data Scientist
  • People Data Scientist
  • Business Analysts - Data Scientistt

Questions and answers

No questions or answers found containing ''.


What is the level of this course? What is the way of assessment of this course? Is it the assignment or exam?


Hi Chris The level is basically all levels i.e. you would be going from the basics to the most advanced levels. For e.g. starting with Python programming moving to Data Visualization to Data Science basics to Data Science advanced, Machine Learning, and Deep Learning. There is no assessment involved. There are projects in the course that you can complete at your pace. Once you complete the courses in the career track to your satisfaction, Uplatz will issue you a Course Completion Certificate.

This was helpful. Thank you for your feedback.

Muwala Chris asked:



Hi Chris Uplatz has a global reputation and is one of the leading IT & Data Science training providers in the world. So it will definitely be a good choice. Team Uplatz

This was helpful. Thank you for your feedback.

Muwala Chris asked:



Hi Chris Uplatz has a global reputation and is one of the leading IT & Data Science training providers in the world. So it will definitely be a good choice. Team Uplatz

This was helpful. Thank you for your feedback.

Muwala Chris asked:




This was helpful. Thank you for your feedback.


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What does study method mean?

Study method describes the format in which the course will be delivered. At courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

What are CPD hours/points?

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on, many of which can be completed online.

What is a ‘regulated qualification’?

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

What is an ‘endorsed’ course?

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body’s logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.