.jpg "Data Science A-Z: Hands-On Exercises & ChatGPT Prize")
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
Preview this Course
Thursday, February 5, 2026
Data Science A-Z: Hands-On Exercises & ChatGPT Prize
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
The Data Science Course: Complete Data Science Bootcamp 2026
- BESTSELLER
- Created by 365 Careers, 365 Careers Team
- English [Auto-generated], French [Auto-generated], 6 more
Students also bought
- The Complete Digital Marketing Course - 12 Courses in 1
- Learning Python for Data Analysis and Visualization
- Python for Data Science and Machine Learning Bootcamp
- The Complete SQL Bootcamp 2020: Go from Zero to Hero
- The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert
Preview this Course GET COUPON CODE
Description
The Problem
Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.
And how can you do that?
Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)
Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture
The Solution
Data science is a multidisciplinary field. It encompasses a wide range of topics.
Understanding of the data science field and the type of analysis carried out
Mathematics
Statistics
Python
Applying advanced statistical techniques in Python
Data Visualization
Machine Learning
Deep Learning
Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.
So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2020.
We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.
Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).
The Skills
1. Intro to Data and Data Science
Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?
Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.
2. Mathematics
Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.
We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.
Why learn it?
Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.
3. Statistics
You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.
Why learn it?
This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.
4. Python
Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.
Why learn it?
When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.
5. Tableau
Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.
Why learn it?
A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.
6. Advanced Statistics
Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.
Why learn it?
Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.
7. Machine Learning
The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.
Why learn it?
Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.
***What you get***
A $1250 data science training program
Active Q&A support
All the knowledge to get hired as a data scientist
A community of data science learners
A certificate of completion
Access to future updates
Solve real-life business cases that will get you the job
You will become a data scientist from scratch
We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.
Why wait? Every day is a missed opportunity.
Click the “Buy Now” button and become a part of our data scientist program today.
Who this course is for:
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
The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
Friday, January 30, 2026
Power BI Course: Build Job-Ready BI & Dashboard Skills
Learn Power BI skills with industry-aligned tools—so you can get job-ready.
Preview this Course
In today’s data-driven workplace, teams don’t just need data—they need dashboards that make decisions easier. Microsoft Power BI is one of the most widely used business intelligence platforms for reporting, analytics, and data storytelling across industries.
This course gives you a practical foundation in Power BI. You’ll start with data connections and simple visuals, then move step by step into Power Query, data modelling, DAX, dashboard interactivity, and publishing to the Power BI Service. Along the way, you’ll work with realistic datasets and build reports that mirror what companies expect in analytics and BI roles.
By the end, you’ll be confident in building professional dashboards, analysing datasets, and sharing insights—preparing you for roles in Data Analytics, Business Intelligence, Reporting, and Consulting.
Skills You’ll Master
Data Preparation: Clean and shape datasets with Power Query.
Data Modelling: Build relationships and analytics-ready models.
DAX & Measures: Create KPIs, calculations, and time-based insights.
Dashboard Building: Design interactive reports with filters and navigation.
Storytelling with Data: Communicate insights clearly with business-ready visuals.
Publishing & Sharing: Deploy reports in the Power BI Service with basic refresh settings.
Benefits
Learn by Doing: Build dashboards using real-world datasets.
Business-Ready Reporting: Create reports that enable leadership to act.
Faster Analysis: Replace manual reporting with automated dashboards.
Scalable Skills: Transfer what you learn to advanced analytics and BI roles.
Portfolio Value: Finish with report dashboards you can showcase.
Microsoft Power BI Dashboard Design: From Clarity to Impact
Design User-Focused Microsoft Power BI Dashboards with Visual Design Principles, Storytelling, and Real-World Project
Preview this Course
Description
Power BI Dashboard Design: From Clarity to Impact – Business Intelligence Mastery
Learn how to create Power BI dashboards that drive real business intelligence insights. This hands-on, practical course teaches you to design dashboards with the end user in mind — from analysts and managers to executives — so your reports are clear, actionable, and decision-ready.
Instead of just building visuals, you’ll learn how to think like a dashboard designer, structure reports for effective data visualization, and transform raw data into meaningful business intelligence insights.
What you’ll learn:
Understand different dashboard audiences and how their needs influence layout, visuals, and interactivity
Build a consistent dashboard design system using color, fonts, spacing, and layout principles
Choose the right Power BI visuals for different types of insights
Format visuals for clarity, impact, and professional presentation
Apply visual hierarchy, white space, and alignment to improve readability
Use storytelling techniques and Power BI interactivity features like tooltips, drill-throughs, and bookmarks
Complete a real-world dashboard makeover project, redesigning an existing report to maximize business intelligence impact
By the end of this course, you’ll be able to design Power BI dashboards that are not only visually professional but also persuasive, easy to understand, and built to support data-driven decisions.
If you want your Power BI dashboards to stand out and deliver real business intelligence value, this course is for you.
Who this course is for:
- Microsoft Power BI users, Data Analysts, consultants, and business professionals who want to design clearer, more impactful dashboards.
Thursday, January 29, 2026
Python for Data Engineers: Pipelines, APIs, Databases A to Z
Build real data engineering pipelines using Python, Pandas, APIs, databases, and scalable coding practices
Preview this Course
Python is the backbone of modern data engineering — yet most learners only scratch the surface.
They learn syntax, write small scripts, and still feel lost when working on real data pipelines.
This course is designed to change that.
“Python for Data Engineers: From Foundations to Production Pipelines” is a complete, hands-on Python course created specifically for data engineering workflows, not generic programming tutorials.
You’ll learn Python from the ground up — but always with a real-world data engineering mindset.
Every concept is explained clearly, coded practically, and connected to how Python is actually used in production data systems.
This is not a shortcut course.
This is not theory-heavy.
This is Python done properly for data engineers.
What Makes This Course Different?
This course teaches:
How Python behaves inside real data pipelines
How data engineers structure, debug, and optimize Python code
How Python interacts with files, APIs, databases, and orchestration tools
How to write clean, reusable, production-ready code
You will not just learn what to write —
you will learn why professionals write Python this way.
What You Will Learn
By the end of this course, you will confidently be able to:
Understand Python fundamentals from a data engineering perspective
Work with core data structures used in real pipelines
Read, write, and process CSV, JSON, and text files correctly
Handle messy, real-world datasets
Write modular, reusable Python functions and packages
Debug errors, implement logging, and handle exceptions professionally
Use Python for data transformation and analysis
Connect Python with databases and APIs
Design pipeline-style programs using object-oriented Python
Build configuration-driven and scalable Python applications
Understand performance bottlenecks and optimization strategies
Learn concurrency, multiprocessing, and scaling concepts
Apply production best practices used in real data engineering teams
Understand how Python fits into Airflow and modern data platforms
Tools & Technologies Used
Python (Core & Advanced)
Pandas
Standard Python Libraries
File-based datasets (CSV, JSON, TXT)
APIs & Databases
VS Code
Virtual Environments
Real datasets and pipeline-style examples
Who This Course Is For
This course is perfect for:
Aspiring Data Engineers
Python developers moving into data engineering
Data analysts who want backend and pipeline skills
Software engineers working with data systems
Anyone preparing for data engineering interviews
Beginners who want a strong, professional Python foundation
No prior data engineering experience is required —
everything is explained step by step, from basics to advanced concepts.
Course Outcome
After completing this course, you won’t just “know Python”.
You will understand how Python is used in real data engineering environments, and you’ll be able to confidently build, debug, and scale Python-based data pipelines.
This course prepares you for:
Real projects
Job interviews
Production systems
Long-term data engineering careers
Learn Python from Scratch: Beginner to Confident Programmer
Master Python fundamentals with hands-on exercises, challenges, and real coding logic with exercises
Preview this Course
This course is designed for absolute beginners who want to learn Python programming from the ground up with a strong focus on fundamentals, logic building, and practical understanding.
Python is a powerful and versatile programming language used in fields such as software development, data science, automation, web development, and more. Instead of trying to cover everything, this course focuses on what truly matters for beginners: building a strong foundation that can be applied to any Python-related field.
Throughout this course, every concept is explained in a clear and beginner-friendly manner using step-by-step explanations and visual thinking. Each major topic is followed by exercises and challenges to help you think independently and apply what you have learned. If you get stuck, detailed solutions with proper explanations are provided to ensure complete understanding.
What You Will Learn
How to run Python programs and set up the development environment
Python syntax, comments, and user input
Variables, data types, and numbers
Strings and string formatting
Boolean values and operators
Conditional statements (if, else, elif)
Lists, tuples, sets, and dictionaries
Looping concepts using while and for loops
List comprehension and logical problem solving
Functions and variable scope
Object-Oriented Programming (classes and objects)
File handling and working with files
Exception handling and error management
Real coding exercises to build confidence and logic
Course Structure
The course is structured in a logical sequence starting from basic concepts and gradually moving towards more advanced topics such as Object-Oriented Programming and file handling. Each section includes practice exercises that encourage you to write code on your own before reviewing the solution.
This learning-by-doing approach helps you understand how Python works internally, not just how to write code.
Who This Course Is For
Complete beginners with no prior programming experience
Students who want to build a strong Python foundation
Anyone interested in starting a career in programming or technology
Learners who prefer structured explanations with practical exercises
By the end of this course, you will have a solid understanding of Python fundamentals and the confidence to continue learning advanced Python topics or move into fields such as data science, web development, or automation.
Python with Machine Learning: Start Building AI Models Today
A Beginner Friendly Guide To Python For AI and Machine Learning
Preview this Course
Python with Machine Learning: Start Building AI Models Today
Machine Learning is transforming the world, and Python is the most popular programming language for building intelligent applications. This course, Python with Machine Learning, is designed to help you start building AI models from scratch, even if you are new to programming or machine learning.
You will learn how to use Python and powerful libraries like scikit-learn, pandas, and NumPy to create real-world machine learning models. The course takes a hands-on, project based approach, so you’ll not only understand the theory but also apply it by building practical AI applications.
By the end of this course, you will be able to implement supervised and unsupervised learning models, evaluate their performance, and gain the confidence to start your journey as a machine learning practitioner.
What you’ll learn:
Fundamentals of machine learning and Python programming
Data preprocessing and feature engineering
Supervised learning: regression and classification models
Unsupervised learning: clustering and dimensionality reduction
Model evaluation, optimization, and performance metrics
Building real-world AI projects using Python
Understanding the machine learning workflow from start to finish
Why take this course?
Hands-on learning with real-world examples and projects
Beginner friendly and step by step approach
Learn Python for machine learning even if you have no prior experience
Gain skills used in data science, AI, and tech industry roles
Who this course is for:
Beginners interested in Python and machine learning
Aspiring data scientists and AI enthusiasts
Software developers looking to add machine learning skills
Anyone wanting to build practical AI models with Python
By the end of this course, you’ll be able to build and deploy machine learning models with Python, opening doors to careers in AI, data science, and beyond.
Enroll Now and Start Your Journey to Python with Machine Learning
Subscribe to:
Comments (Atom)