The number of employed data scientists increased from 30% to 60% between 2020 and 2021.
Demand in data science is growing because of the need to uncover valuable insights from the massive volume of generated enterprise data.
It gives companies competitive leverage with accurate business predictions and robust reporting. Getting started in data science can be quite overwhelming.
Some insist you must master statistics, calculus, database, natural language processing, and the like, to become a data scientist.
Data science involves using data for exploration, problem-solving, and insights.
Simply put, it consists of answering exciting questions using data.
It takes this simple workflow:
Question → Gather data relevant to the question
→ Clean the data → Analyze & visualize the information
→ Create and monitor a machine learning model → Present results.
You don’t necessarily need advanced calculus and other subjects mentioned above.
But, it does require a basic grasp of programming language and simple math to get started. The advanced skills will be necessary when growing in your career.
1. Get Easy With Python
Python and R are incredible choices for programming in data science.
But, R is standard in academia, while Python is popular in the industry sector. Both have great packages for supporting data science workflows and tasks.
As a beginner, you only need to pick one.
We recommend Python because it’s one of the industry’s most sought-after languages.
It will give you access to comprehensive entry and high-skilled level data science roles. You can choose from data engineer, analyst, architect, statistician, database administrator, and a lot more. Whatever the role, you would also need firm Python grounding to upskill and fill it.
Consider mastering these Python fundamentals, and the rest will fall in place as you continue learning.
●Data types and structures
●Functions, loops, comprehensions, and conditional statements
2. Know the Key Data Science Libraries
With a firm grounding in Python basics, you can move into learning data science libraries that are critical for your workflow.
●NumPy simplifies numeric computation and supports other libraries as well.
●Pandas is built in NumPy. It offers robust data structures and supports explorative analysis.
●Matplotlib is a visualization tool that doubles up as a plotting solution. Although effective and highly efficient, it may be overwhelming for a beginner. Instead, you can opt for the Seaborn library.
●Sci-kit Learn is a popular and general-purpose library for machine learning in Python. It has many modules for pre-processing, data cross-validation, etc.
●Seaborn is an alternative to Matplotlib allowing easier plotting and visualization.
3. Use Pandas to Learn Data Analysis, Manipulations, and Visualizations
After learning the standard libraries, it is time to apply them. You can start with pandas to conduct data analysis, visualizations, and other manipulations.
Pandas allow you to handle tabular data like Excel or SQL tables. It can:
●Read and write data
●Manage missing data
●Clean and filter data
●Merge data sets
●Run data visualization
4. Use Scikit-Learn to Understand Machine Learning
Scikit-Learn library allows you to answer several questions in machine learning, such as:
●Which machine learning (ML) model works best with my data set?
●How do you interpret ML model results?
●Which are the best features to include in my ML model?
To have a commanding proficiency in machine learning, you would need experience and advanced knowledge. We recommend the following resources for basic and advanced knowledge on machine learning using Python.
●An Introduction to Statistical Learning offers theoretical and practical learning on regression and classification concepts without the need for advanced mathematics. There is a supplementary video for the resource for a better learning experience.
OpenIntro Statistics is a suitable refresher resource on probability or statistics.
5. Continue Learning and Practicing
As with most successful endeavors, you should be able to identify your passion and fuel it.
To constantly hone your data science skills, identify what drives you and act on it. Here is a list of actions you can take to nurture and further develop your skill set.
A. Participate in Kaggle Competitions
Kaggle is a platform that conducts data science competitions amongst users. Most of the competitions are not real-world data science tasks, but they are incredible learning resources.
The standard competition is too challenging for beginners. But, the site allows you to compete and learn based on your skill level. You can learn more about Kaggle to apply and improve your skill.
B. DIY Data Science Projects
Another way of applying and improving your skills is by doing personal data science projects. If you’re stuck on Python DIY project ideas, you can check this resource to get the push for action.
Remember to share your project and its write-up on GitHub to demonstrate to others your ability to deliver a data science project.
C. Join Professional Data Science Associations
Apart from GitHub engagement, you should experience joining a Python community. There are several Python conferences and associations you can choose from. We highly recommend PyCon and its affiliates across the world. For a data scientist, a PyData conference should be a top priority.
Getting started in data science from scratch can be overwhelming. But the appealing and increasing data science roles should be an incentive to take the step anyway.
Python is instrumental in data science. We recommend a less overwhelming path with maximum impact to help move from novice to beginner, and quickly scale up to advanced level.
Be patient with Python fundamentals and learn the critical libraries like Pandas and Scikit-Learn. For further knowledge and honing your skills, DIY projects, Kaggle competitions, and attending professional conferences can be helpful.