9 proven IT courses on Udemy

In the ever-evolving world of technology and data, the demand for skilled data science and analytics professionals has skyrocketed. Whether you’re a beginner looking to dive into the field or an experienced practitioner looking to hone your skills, the range of courses available can be both exciting and overwhelming. In this digest, Sergey Filatov, Analytics Lead at QIC digital hub, presents a curated selection of courses that cater to a variety of needs and skill levels in data science, machine learning, business intelligence and more.

Sergei Filatov, Lead Product Analyst at QIC digital hub, specializes in predictive modeling and data visualization. A nominee for Forbes 30 under 30 in 2023, he holds a Master’s degree in Financial Analysis and a Bachelor’s degree in Business Informatics. He has worked with companies such as Estée Lauder Corp and MTS. He writes courses for platforms such as Coursera and Skillbox, and is a senior lecturer in the MBA programme at HSE, focusing on product analysis and financial modeling.

From comprehensive training in Python for data analysis to specialised courses in tools such as Microsoft Power BI and Tableau, each course in this digest offers unique benefits and addresses specific learning objectives.

1. Complete Data Science Training with Python for Data Analysis

This course appears to be a comprehensive introduction to data science with Python, covering areas such as mathematics, statistics, Python programming, advanced statistical techniques, machine learning, and deep learning. Designed for beginners, it emphasizes on delivering a bunch of interconnected topics that seamlessly complement each other and equips you with the complete set of tools essential for becoming a data scientist from scratch.

Pros:

• Comprehensive curriculum covering a wide range of data science topics
• Suitable for beginners with no prior experience required
• Includes practical examples and real-life business cases

Cons:

• May be overwhelming for those completely new to programming or data science
• The breadth of content might mean less depth in some specific areas

2. Python for Data Science and Machine Learning Bootcamp

If you have previous programming experience and want to deepen your understanding, this course will be worth your while. It focuses on using Python for various data science and machine learning applications. It also might include training on libraries like NumPy, pandas, matplotlib, and machine learning frameworks like scikit-learn.

Pros:

• Practical approach to learning Python specifically for data science and ML
• Coverage of essential Python libraries and ML frameworks
• Suitable for those who want to deepen their Python skills in a data science context

Cons:

• Requires some basic knowledge of Python
• Might not cover the theoretical aspects of machine learning in depth

3. Data Analysis with Pandas and Python

This course focuses on using Pandas — the most popular Python library in the world. It’s a good fit for those aiming to take skills in filtering, pivoting, munging, merging, visualizing and more to the next level as it covers hundreds of various methods, attributes, functionalities and features.

Pros:

• In-depth coverage of the pandas library, a cornerstone in Python data analysis
• Practical examples to apply learning

Cons:

• Focuses mainly on one library, so broader data science topics might not be covered
• Assumes some prior Excel and Python knowledge

4. Machine Learning, Data Science, and Generative AI with Python

If you possess some programming or scripting experience, this course will be useful by instructing in the techniques used by actual data scientists in the tech industry. It delves into machine learning techniques, data science applications, and possibly the emerging field of generative AI, all using Python. In addition, there is a dedicated section on machine learning with Apache Spark, allowing you to apply these techniques to analyze big data on a computing cluster.

Pros:

• Covers advanced topics like generative AI
• Practical Python applications in ML and data science
• Walks through installing all the necessary softwares
• Course content is derived from practical experience

Cons:

• Requires a solid level of high school math skills, Python and scripting skills
• Generative AI might be a complex topic for beginners

5. Microsoft Power BI — The Practical Guide 2024

This course teaches all the tools of Microsoft Power BI, a tool for business intelligence, ways to work in the different views of the Data Model and understanding the Query Editor. Designed for individuals seeking to understand the creation of personalized visuals, this course enriches with skills in analyzing diverse data sources and creating unique datasets, as well as exploring advanced features of Power BI.

Pros:

• Focus on a leading tool in business intelligence
• Practical and up-to-date with the latest features of Power BI
• Offers two approaches to excel in the course based on the time you have available

Cons:

• Difficult to comprehend the material even with mid-level in coding and visualization
• Specific to Power BI, so knowledge might not transfer directly to other BI tools

6. Advanced DAX for Microsoft Power BI Desktop

The course programme is tailor-made for those who want to elevate their current Power BI proficiency by mastering Data Analysis Expressions (DAX). It progresses to offer valuable tips and best practices for DAX power users and provides insights into performance tuning and DAX query optimization with the help of such tools as DAX Studio and Power BI’s Performance Analyzer.

Pros:

• Covers a comprehensive list of DAX functions
• Enhances skills in data modeling and analysis within Power BI
• Lectures are accompanied with practical demonstration

Cons:

• Very specific focus, not for beginners in Power BI
• The content is challenging to grasp for new-to-DAX analysts
• Vague assignments prompts
• Course is not available for Mac users

7. Tableau 2022 A-Z: Hands-On Tableau Training for Data Science

This course covers Tableau from basic to advanced levels, teaching you how to work with data extracts, use aggregations, create various charts, maps, interactive dashboards and explore the latest features in data preparation. Upon completion, you’ll be a highly proficient Tableau user, equipped with the skills of a data scientist to extract, analyze, and visualize complex questions effortlessly.

Pros:

• Comprehensive coverage of Tableau, a key tool in data visualization
• Hands-on approach suitable for learners from the very basics
• Each section is broken down into short manageable video lectures

Cons:

• Focused on Tableau only, other aspects of data science are not covered
• Might be too basic for those already familiar with Tableau
• Earlier sections are not detailed enough for beginners

8. Tableau 2022 Advanced: Master Tableau in Data Science

This course offers a deeper glimpse into analytics as it teaches multiple crucial skills, such as usage of Dynamic Sets, Table Calculations, Animating in Tableau and much more. It comprises 75 lectures followed by practical demonstration and a handful of training exercises that will help you memorize the material in the long term.

Pros:

• Advanced content for deep learning of Tableau
• Suitable for enhancing professional data science skills with Tableau
• Doesn’t require a specific sequence for completing the course

Cons:

• Demands prior knowledge in Tableau and the software itself installed
• Highly specialized, might not cover broader data science topics
• Not friendly to non-native English speakers

9. Google BigQuery for Marketers and Agencies — 2022

A course tailored for marketers, campaign managers and SEO/SEM specialists, focusing on using Google BigQuery for data analysis. It aims to narrow the gap between data analysis and digital marketing, guiding learners towards becoming a decision-maker driven solely by data. Subsequently, the course delves into two hands-on projects, allowing to apply and consolidate newfound knowledge.

Pros:

• Specialized focus on BigQuery for marketing applications
• Practical for professionals in marketing and related fields

Cons:

• The lecturer is repetitive and not enthusiastic at all
• Lack of practical tasks
• Requires some background in marketing and data analysis
• Very specific focus, not suitable for those looking for a broad data science education

Navigating the world of data science education can be a challenging endeavour, given the vast array of courses available, each with its own focus and prerequisites. When choosing the course, keep in mind that the best one for you depends on your current skill level, learning style, and professional goals. Choose wisely and embark on a journey that not only enhances your knowledge, but also opens doors to new opportunities in the dynamic field of data science.