Congrats on Completing your Data Science Training, This is Next …

Image generated by AI Image Generator Dall-E, Query — “a bridge on a frozen lake”

“Success is not final… it is the courage to continue that counts.” — Winston Churchill

Perhaps you have taken a data science program from Great Learning, Coursera, Datacamp, or Udemy, and you are now at this point wondering — What next? First, I will like to congratulate you — you are now among the few who started, and with a lot of tenacity persisted to completion. Kudos!

The next step is simply the milestone towards further growth and achievement and perhaps some good rewards for your persistence. I will guide you on some of the next steps you can take to achieve your desired growth and especially plucking the rewards.

The next step in data science will involve any of the following:

• Career change and thus interview preparation — here you will need to access a lot of interview prep guides, thanks to the internet there is never a shortage of materials to prep with
• Internship to solidify the learning journey, sometimes practising data science is not in the same cluster as learning data science — so this step is extremely important
• Go competitive — become among the best in the field, by signing up for Data science competitions. More on this later.
• Lastly, specialize in an aspect of data analytics with the goal of becoming an expert.

It is expected that you would have been exposed to the interesting intricacies of the following aspects of data science while learning

• Mathematics and statistics: Data scientists use mathematical and statistical techniques to analyze and model data, and to make predictions and decisions based on data.
• Programming and computer science: Data scientists use programming languages such as Python, R, and SQL to clean, preprocess, transform, and analyze data.
• Data visualization and communication: Data scientists use data visualization tools and techniques to communicate insights and results to non-technical stakeholders.
• Machine learning and artificial intelligence: Data scientists use machine learning and artificial intelligence techniques to build predictive models and systems that can make decisions based on data.
• Data engineering and infrastructure: Data scientists work with data engineers to design and implement scalable and efficient data pipelines and infrastructure.
• Business and domain knowledge: Data scientists need to have a good understanding of the domain of research or experimentation.

Irrespective of the path followed, the ultimate trick to progressing in data science is — the doing of deeds aka PRACTICE, you need to relentlessly practice your skills through application to real-life problems.

Following the rigour of your learning journey, there is the need for you to identify the aspect of data science or analytics that will be the next relevant milestone, as a recap — the following are various aspects of data science you can specialise in:

1. Problem formulation: This stage involves defining the problem that needs to be solved and establishing the project goals.
2. Data collection: In this stage, relevant data is gathered from various sources and stored in a suitable format.
3. Data preparation: The collected data is then cleaned, preprocessed, and transformed to make it suitable for analysis.
4. Data exploration: The cleaned data is analyzed to gain insights and identify patterns, trends, and relationships between variables.
5. Model building: In this stage, various machine learning and statistical models are built and trained on the data.
6. Model evaluation: The performance of the models is evaluated using suitable metrics and the best model is selected and refined in a continuous process.
7. Model deployment: The selected model is deployed in a production environment, where it is used to generate predictions or insights.
8. Model monitoring and maintenance: The model is monitored regularly to ensure that it continues to perform well and that maintenance and updates are performed as needed.
9. Communication and visualization: The insights and results obtained from the data science project are communicated to stakeholders in a clear and concise manner using appropriate visualizations and storytelling techniques.

Irrespective of the path followed, the ultimate trick to progressing in data science is — the doing of deeds aka PRACTICE, you need to relentlessly practice your skills through application to real-life problems.

Generally, take any of the following steps to advance a career in data science:

Step 1 — Build a Portfolio: Develop a body of work you can easily showcase with a single link(this can be on Github or LinkedIn), and highlight the data science projects worked on (this can include some of the content of your learning program and more). This is necessary for showcasing your skills and expertise to potential employers or clients.

Step 2 — Network: Attend industry events, join online communities, and connect with professionals in the field. Building a network can help you stay up to date with the latest industry trends, and may lead to job opportunities. Check out the comprehensive list from Purdue University.

Step 3 — KEEP Practicing with Real-World Data: Look for opportunities to work with real-world data. You can participate in data science competitions, contribute to open-source projects, or volunteer with non-profit organizations. You can try — Kaggle, Innocentive, Zindi, DataDriven, Omdena and Codalab.

Step 4 — Internship: This involves honing your skills based on organisational requirements. This step is essential for folks interested in career transition. You can take a virtual internship from any of the following:

• Google Search — “virtual data science internships” to have a list of interesting opportunities around you
• Take the KPMG Virtual Internship
• Take the Sparks Foundation Virtual Internship
• Join the LetsGrowMore Data Science Virtual Program

in order to solidify your profile as a data scientist.

Step 4 — Keep Learning: Data science is a rapidly evolving field, so it’s important to keep up to date with the latest trends, technologies, and techniques. Consider taking additional courses, attending workshops, or obtaining certifications. This is the part about specialization, specialising in any given aspect of data science, and striving to be competent and dangerous in that field.

And to the interesting part, getting the rewards

Image Generated by AI Image Generator — DALL-E with the query ”getting huge dollar rewards in a bag”

Step 5 — Look for Job Opportunities: Start looking for job opportunities that align with your interests and skill set. You can search for job openings online, attend job fairs, or work with a recruiter. For folks the completed the Great Learning Programs, endeavour to use the Excelerate Menu on your Dashboard for Career Prep and Guidance before applying for jobs, especially the Resume Review. Personally, I find the Interview prep here here and here very useful for 2023. To prepare for your job interview, you can use Summary Cheats here.

Step 6— Take part in Competitions and Hackathons: There are now a lot of platforms where you can earn as much as $100,000 or more for getting top solutions, however, don’t expect it to be easy — the whole world of data scientists will want a piece of that pie. You can start with Kaggle, Zindi and more here

Step 7 — Practice Your Communication Skills: Data science is not just about crunching numbers, it also requires effective communication. Practice communicating your findings to both technical and non-technical audiences.

Lastly, data analytics, business analytics, and business intelligence all under data science are competitive fields, so it may take some time to land your dream job. Stay persistent and keep working on your skills, and you’ll be well on your way to a successful career in data science!

ChatGPT contributed to this article.