In the article “Data Scientist — 12 Steps From Beginner to Pro” I described how to master a profession from scratch. In this article, I will focus on the key skills required to become a Data Scientist.
👨🎓️📊 Data Scientist — 12 Steps From Beginner to Pro
12 steps for those looking to build a career in Data Science from scratch. Below there is a guide to action and a…
1. Mathematical base
Knowledge of machine learning techniques is an integral part of the Data Scientist job. Working with machine learning algorithms requires an understanding of the basics of calculus (for example, partial differential equations ), linear algebra, statistics (including Bayesian theory), and probability theory. Knowledge of statistics helps the Data Scientist to critically assess the significance of data. The mathematical base is also important in developing new solutions, optimizing and adjusting the methods of existing analytical models.
Free online courses in the following areas of mathematics with high student ratings:
- Intro to Descriptive Statistics
- Bayesian Statistics: From Concept to Data Analysis
- Data Science Math Skills
- Mathematics for Data Science
Collecting, cleaning, processing, and organizing data are also important skills of a Data Scientist. For these tasks and the implementation of the machine learning models themselves, the programming languages Python and R are used. How to get started with Python, I discussed in the article “I Want to Learn How to Program in Python. Where to Begin?”.
I Want to Learn How to Program in Python. Where to Begin?
We will tell you how to proceed if you decide to master Python 3 from scratch: what to learn first, where to get…
- Python Programming
- Python Tutor
- Fundamentals of Python Programming
- DataCamp (eng.)
- The Python’s Class google (Eng.)
3. Working with databases
Most Data Scientist tasks require programming skills using the SQL query language. Despite the fact that NoSQL and Hadoop are also an important part of Data Science, SQL databases are still the main way of storing data. The Data Scientist must be able to produce complex queries in SQL.
Call me crazy, but I want to teach SQL to every data professional of any kind. I’m talking about people from HR, IT, sales, marketing, finance, vendors, and so on. If your goal is to make the most of your data-driven work, the Excel + SQL combination allows you to do amazing things. If your goal is to move into analytics (for example, as a business analyst), you definitely need SQL skills […] Why not start learning SQL this weekend?
4. Data preprocessing
Data Scientist also prepares data for analysis. Often data in business projects is not structured (videos, images, tweets) and not ready for analysis. It is imperative to understand and know how to prepare the database to obtain the desired results without losing information. During the exploratory data analysis (EDA) phase, it becomes clear what data problems need to be addressed and how the database needs to be transformed to build analytical models.
To work on creating machine learning projects, you will need knowledge of classic machine learning algorithms such as linear and logistic regression, decision tree, support vector machine. The following courses will help you understand the intricacies of machine learning algorithms:
- Algorithms: theory and practice. Methods
- Machine Learning Algorithms: Supervised Learning Tip to Tail (eng.)
6. Skills specific to the selected field of analysis
After gaining basic knowledge, you will need specific skills for your chosen field of work. For example, deep learning is a class of machine learning algorithms based on artificial neural networks. These techniques are commonly used to create more complex applications such as object recognition and generation algorithms, image processing, and computer vision. So it is a good idea to be aware of new state-of-the-art algorithms and solutions in different areas of both machine and deep learning.
Some useful resources here are:
Deep Learning Digest
A weekly digest of the new state-of-the-art (SOTA) Deep Learning approaches and solutions
AI In Plain English
Where Artificial Intelligence, Machine Learning, Data Science and Big Data get together.
🔊 Soft Skills 🔊
7. Ability to convey your idea
The Data Scientist must be able to communicate the message to a wide audience. This is especially important in the business area, where project customers may not have technical skills and terminology. Presentation of the results will require the skills of presenting information, the ability to convey the idea in simple language. Participate in Data Science conferences and online meetups. This is an opportunity not only to improve communication skills and small-talk with colleagues but also to get feedback.
Courses on principles of a successful presentation:
- Data Analysis and Presentation Skills: the PwC Approach Specialization;
- Communicating Business Analytics Results — University of Colorado course;
- A Data Scientist’s Guide to Communicating Results is a guide to mastering effective presentation skills.
The Data Scientist profession involves teamwork on projects. This requires communication skills and a clear vision of their own role in the team. The successful outcome of a collective project directly depends on the effective interaction of the participants. The ability to hear a different opinion and make a joint decision is also important for team participation in Data Science Kaggle competitions.
Data Science is a team sport, and those who say “hitters are the best!” Are likely to face rebellion from the rest of the team. Every team member is valuable! If everyone plays their part well, then the business will continue to derive value from data.
Successful teamwork comes with experience, and to master the intricacies, check out the following resources:
- Working in Teams: A Practical Guide — a course on the intricacies of teamwork and conflict resolution;
- book 17 irrefutable laws of teamwork John Maxwell;
- Design Team Behavior Patterns — Guided by Tom DeMarco and Timothy Lister.
9. Ability to see the commercial side of the issue
A key Data Scientist skill for working in a business environment is the ability to find cost-effective solutions with minimal resource costs. Companies that use Data Science for profit, need for specialists who understand how to implement business ideas with data.
As organizations begin to fully capitalize on internal information assets and explore the integration of hundreds of third-party data sources, the Data Scientist’s role will continue to grow.
About the features of Data Science for business:
- Data Science for Business (English) — an interactive course from DataCamp;
- A Guide to becoming Business-Oriented Data Scientist is a guide to the intricacies of Data Science in business applications.
10. Critical thinking
The skill of critical thinking helps to find approaches and solutions to problems that others do not see. Data Scientist critical thinking is about seeing all sides of a problem, considering data sources, and showing curiosity.
The Data Scientist must understand the business problem, be able to model and focus on what matters to solve it, not what is outsider and can be ignored. This skill, more than anything else, determines the success of the Data Scientist.
If you are looking to build a career as a Data Scientist, get started now. This area is constantly expanding and needs new specialists. To master the essential Data Scientist skills from scratch, enroll in the free online Data Science courses mentioned here, and become a professional ✨Data Scientist✨.
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