Expert Advice on how to Build a Successful Career in Data Science.

INTRODUCTION

This is an Industry that can be immensely rewarding, providing an intellectually challenging and stimulating environment. Data scientists must keep ahead of the latest and emerging technologies and developments. Building a successful career in this field is not as hard as some people think and it's not easy, it requires focus mind, curiosity and analytical mind, time and a lot more.

Data Science Skills You Need To Know

To succeed in data science you'll need an assortment of technical and non-technical skills.

1. Technical skills- skills you'll need to develop to become a data scientist;

      1. Programming: Be proficient in Python and R which are mostly used in data manipulation, statistical analysis and machine learning.
   
      2. Mathematics: Have knowledge in linear algebra, calculus and optimization techniques which are essential in machine learning and algorithms.
   
      3. Data visualization: Be able to transform your data and findings into visual formats using tools like Power BI and Python libraries like seaborn and matplotlib.
   
      4. Machine learning: You should be able to apply and understand different machine learning algorithms like supervised and unsupervised learning to predict outcomes and show patterns.
   
      5. Data Transformation: Transforming your data into more useful form using feature engineering techniques and normalization.
   
      6. Data Wrangling: Ability to clean, structure and enrich raw data into a desired format for analysis. Handling missing values, outliers and merging datasets.
   
      7. Deep Learning: Understand neural networks and deep learning frameworks for tasks that require image recognition, NLP.
   
      8. Model Deployment: Skills on how to deploy your models into production environments.
   
     9.  Business Intelligence: Use BI tools and techniques to analyze data, produce reports and decision making.     
   
     10. Analytics and Modeling: Be able to apply various analytical and modeling techniques to understand data, make predictions, and test hypotheses.
   
     11. Big Data: Manage and analyze large volumes of data with big technologies, understand the complexities and challenges of big data environments.
   

2.Non-Technical skills- These are the human skills, they are cross-functional that are very necessary;

Communication: Being able to translate your findings into clear, concise, and actionable insights for technical and non-technical stakeholders.

Business Acumen: Understand business processes, goals and strategies to align data projects with organizational objectives.

Teamwork: Engage and work well with your team members, always participate and contribute to the work.

Critical Thinking: The ability to approach problems logically, question assumptions, and evaluate the strengths of arguments.

Curiosity: A natural curiosity to ask questions, explore data for hidden patterns, and a continuous desire to learn and discover new techniques.

Problem-solving: Defining a problem, determining the cause of the problem, prioritizing and selecting alternatives for a solution.

Time management: Manage your time well. Plan which topic or which side you'll tackle first, focus to finish the task. Use at least 4hours a day just for coding because the more you code the more it sticks in your mind. 

Tips to Become A Data Scientist

To make your career plans easier to navigate, here are a few major tips to become a Data Scientist;

a. Fundamentals: Start by learning the basics, you can't start from the advanced concepts, gaining the fundamental knowledge will provide you with a strong foundation for advanced concepts.

b. Programming Skills: Familiarizing yourself with one or two languages and gaining proficiency in them will help you a lot in going forward.

c. Build a strong foundation in data manipulation: Learn the techniques of cleaning, transforming, and preparing data for analysis.

d. Focus on Machine Learning: Have a strong foundation in ML and learn about the various algorithms and their working mechanism.

e. Build Your Portfolio: Having a portfolio is always the important thing as a tech person because that is where you showcase your skills and projects you have done from the moment you began your career.

g. GitHub account: Make sure you create a GitHub account to keep all of your projects

h. Network and Learn from Others: Always be ready to network and to learn from anyone. Attend meetups, hackathons, conferences in order to be updated with the latest trends. Networking helps a lot and it has worked for so many techs I being among them. This includes connecting via social media accounts like LinkedIn.

i. Practice critical thinking: This helps in analyzing data and solving complex problems. You can develop this skill by asking questions, testing hypothesis and challenging assumptions.

j. Develop communication skills: Data scientists need to communicate complex findings to non-technical stakeholders

k. Practice ethical data science: As you access data practice ethical data science by ensuring data privacy, accuracy, and fairness.

EDUCATIONAL BACKGROUND.

Building a Data Science career is a journey from several educational backgrounds. Everyone prefers a certain platform that works best for him/her.

• Earning a degree from a certain university is a good start for a data science course but it is not the best since you're not always taught everything. The university gives you 25% and 75% is yours to do.

• We also have free online websites that offers free courses and certifications in data science from being a beginner to expert sites like Coursera, edX, Udemy, FreeCodeCamp and many more.

• There are also Data science Bootcamps that offer intensive training to help you quickly acquire practical skills. Udemy, Coursera is among the sites that offers Data Science Bootcamps.

• YouTube is one of the best platforms you can learn from, its like an online university as long as you have an internet connection. It has everything you need about data science that is tutorials coding together with the tutor helps you understand more about what you're doing.

JOB SEARCHING AS A DATA SCIENTIST.

There are many data scientist who have graduated from good universities but they keep on searching for jobs. The easiest way to find a job as a data scientist is to give a client what they need.

CONCEPTS
Have a Portfolio: If you're looking for a serious job in this field, do some projects with real data. You can post them on your GitHub account that you must Have. Apart from Kaggle competitions, Zindi competitions, find something that you love or a problem you want to solve and use your knowledge to do it.

Look for an Internship before you start applying for a job. This help you to have an experience before doing the real job. Also you will have something to place on your resume.

Be Patient: If you apply and don't get the job, be patient keep trying as long as you have the necessary skills then there is no doubt you'll get a job.

Be Prepared: In any skill be it technical or non-technical, remember that you'll be important in a certain organization, dealing with different people, be ready to answer questions about how you'll behave in different situations at work.

LinkedIn Account: Ensure to have a LinkedIn account. This will help you to connect with different data scientists around the globe as you share your knowledge a client might be interested in your work and he/she reaches out to you.

Ask and search different data scientists to know what they do at work and how they got to secure there jobs in order to have an idea of what to do.

NOTE Always keep in mind that;

Practice makes perfect
Put what you learn into practice by doing projects, participating in competitions, hackathons. Share what you've built, be ready to learn, corrected, disappointed and more. Kaggle, Zindi, Analytics Vidhya, hackerEarth, Devpost and many more hosts knowledge competitions, hackathons you can join to boost your skills, earn badges and learn more.

As long as you're in Tech field, new technologies will keep on emerging, keep learning.
Good luck! in your Data Science Career.....