The Ultimate Guide to Getting Started with Data Science
“Hello, World!!” When learning to code, this is most likely the first program that every tech newbie writes.
Late last year, I made the decision to pursue a career in Data Science. I’ve always wanted to learn how to code. However, I didn’t know how to get started, coming from a finance and accounting background. I knew my passion was in technology while I was in uni pursuing my undergraduate degree, but I had no idea that someone could switch careers. After completing my undergrad degree, I was fortunate enough to land a data analytics role. While I was at it, I decided to learn to code by utilizing YouTube resources. At this point, I was really perplexed because everyone had their own approach to learning Data Science. I decided to look for Data Science schools in Kenya.
While strolling at the mall one evening, I stumbled across some people. They welcomed me to their booth and began introducing themselves to me. “I’m ‘Ashley’ from Moringa School, and I’d like to inform you about some of the courses we have available.”She swiftly handed me a booklet. I took the pamphlet and went about my ‘shenanigans’ in the mall.
To keep the long story short, I applied and was accepted into the Moringa School program. I was excited because I was so passionate about Data Science, but I was also nervous because I had never coded in my life and had no idea what to expect.
To begin, Data Science is an interdisciplinary field that combines domain knowledge, programming skills, and math and statistical knowledge to derive meaningful insights from data.
And you might be wondering, “What is AI?” at this point. What is Machine Learning, and how does it work? What is Deep Learning, and how does it work? What are Neural Networks and how do they work?
AI is any technique that makes a computer smart enough to mimic human behavior.
Machine learning is the art of giving the computer the ability to learn and make decisions from data without being programmed. It is seen as a part of artificial intelligence.
Examples of machine learning:
- Facial detection & recognition on devices
- Medical diagnosis tools used by the NHS
- Spam/non-spam filtering on emails
But what is the ultimate way to get started with Data Science?
When it comes to getting started with Data Science, many people are perplexed and unsure of which road to take. Everyone learns in a different way, but this is what I would do if I had to start all over again.
- Learn programming in Python
Python and R are excellent choices for Data Science programming languages. Python is a computer language that is both simple to learn and widely utilized in the industry. Python is my preferred programming language since its syntax is simple to learn and easy to use.
2. Learn basic statistics for data science.
Statistics is a well-known subject that focuses on data collection, data management, data analysis, data interpretation, and data visualization, among other things. The fundamental concept of machine learning and data science is statistics. As a result, it is really important to master the principles of statistics in order to tackle real-world problems in the field of data.
3. Start projects as soon as you can.
Projects are the most effective way to grasp this field. Datasets are widely available on the Zindi and Kaggle websites. Start exploring different projects because this is the most effective approach to building your portfolio.
4. Work on more advanced projects
Work on projects involving advanced concepts such as deep learning, natural language processing, neural networks, and computer vision. When it comes to Data Science, Deep Learning, Natural Language Processing, and Computer Vision are largely overlooked topics. If I had to start over, I would focus on more NLP and computer vision projects.
5. Learn SQL and databases.
SQL (Structured Query Language) is a powerful programming language for interacting with databases and extracting diverse types of data from the Database. To thrive as a data scientist or a machine learning professional, you’ll need working expertise in databases and SQL.
6. Join Data Science Communities.
Data science is a good career path, but it can also be a lonely one. Connecting and networking with industry experts from all over the world is always good. As a result of my investigation, I’ve discovered that data has a myriad of communities. People are incredibly generous and willing to mentor others. I joined a number of data communities, to name a few; Nairobi Women in AI and Machine Learning, Data Science East Africa, Coding Black Females, Black Valley, Code First Girls, Zindi, and many more. Joining these communities was the best decision I ever made.
Lastly, believe in yourself because you are capable. Set SMART goals and be consistent in your learning journey. Reach out to people for help whenever you are stuck and NETWORK! NETWORK! NETWORK!
#Happycoding
Brenda x
