Curriculum To Learn Data Science

Unlike much other science, you can learn data science online. You don’t need specialized labs and in-person tutors to get started.

You’d only need a cloud account even for more sophisticated use cases. Create an AWS EC2 instance and use it only when needed. You get high-performance computers at an affordable cost.

But you have a significant drawback when learning data science for yourself.

It’s a vast area to get lost. And the internet has more than you need about data science. The number of Youtube videos, courses, and certifications is overwhelming.

Thus you should have a curriculum for yourself and stick to it. Of course, what’s outside your plan may seduce you along your journey. But it’s essential to stay on the course.

A curriculum provides structure and a roadmap for your learning. You know exactly what to learn, when, and what to do next. You get this challenging job done when you’re learning in a University, Udacity nano degrees, or Coursera specializations.

But even then, your curriculum should have a more strategic choice. No one would create it for you but you. It’s yours and unique to you.

This blog post will outline the steps necessary to build a curriculum. Let’s also explore some tips on developing good habits to help you reach your peak performance quickly.

Pick a career path: There are many in data science.

Data science has evolved a lot, and it includes so many career paths within it. Finding what interests you early will help you outline your curriculum. Here are the prominent roles and their usual responsibilities.

As you’d see in the following list, the differences in duties or skillsets needed are subtle for some functions. In the real world, the boundaries are blurry, and the expectations from these roles vary too.

Data Scientist

Develops predictive models and statistical analysis to drive decision-making insights. They work with Business Analysts and Data Engineers to solve various business issues.

To become a data scientist, you must learn the following:

Mathematics & Statistics

Programming languages such as Python, R, SQL, and SAS
Big data processing tools such as Hadoop and Spark
Data visualization tools such as Tableau, PowerBI, and QlikView

Machine learning algorithms

Data Scientists’ responsibilities include the following:

Cleansing and preparation of data

Perform exploratory data analysis
Build predictive models
Communicate findings to business stakeholders

Data Analyst

Leverages data to help organizations understand their customers better. They collect, organize, analyze, and present data using statistical and visualization techniques.

Data analysts are excellent in:

Programming languages such as SQL, Python, and R
Data visualization tools such as Tableau, PowerBI, and QlikView
Microsoft Excel

Statistical techniques

The data analyst role has the following responsibilities:

Collecting data from multiple sources

Perform exploratory data analysis
Answering business questions through data
Presenting findings to business stakeholders

Data Engineer

Builds the platform that houses an organization’s data. They design, construct, maintain, and troubleshoot the data infrastructure.

To become a data engineer, you must sharpen your skills in the following area:

Programming languages such as Java, Python, and Scala
Big data processing tools such as Hadoop and Spark
Relational and NoSQL databases such as MySQL, MongoDB, and Cassandra

Data modeling and ETL (Extract Transform Load) processes

The responsibilities of a data engineer often include the following:

Designing and maintaining the data infrastructure

Building data pipelines to collect and process data
Ensuring the quality and accuracy of data
Optimizing data storage and retrieval

Machine Learning Engineer

Develops and maintains machine learning models. They work with Data Scientists to turn insights into products or services.

The skillset of a machine learning engineer includes:

Programming languages such as Python, R, and Java
Machine learning algorithms and libraries such as TensorFlow and Scikit-learn
Big data processing tools such as Hadoop and Spark

The responsibilities of a machine learning engineer include the following:

Developing and training machine learning models
Deploying and monitoring machine learning models

Tuning machine learning models for improved performance
Collaborating with data scientists and other engineers to solve business problems

Database Administrator

Manages an organization’s databases. They optimize performance, ensure security, and implement backup procedures.

The skillset of a database administrator includes:

Relational databases such as MySQL, Oracle, and MS SQL Server

NoSQL databases such as MongoDB, Cassandra, and HBase
Database performance tuning
Backup and recovery procedures

Database administrators have the following responsibilities:

Installing and configuring databases
Loading data into databases

Optimizing database performance
Ensuring database security
Implementing backup and recovery procedures

Product Manager

Oversees a product’s development lifecycle, features, and roadmaps. They collaborate with designers, engineers, and other stakeholders to bring a product to market.

The skillset of a product manager includes:

Product management tools such as JIRA and Asana
Project management techniques
Data analysis skills

Communication and presentation skills

The responsibilities of a product manager include the following:

Developing product strategy and roadmaps

Creating product requirements and specifications
Working with designers and engineers to build the product
Launching and managing the product lifecycle

Analyzing product performance and making improvements

Business Analyst

Understands business processes and translates them into data requirements. They work with Data Scientists and Data Analysts to develop actionable insights from data.

The skillset of a business analyst includes:

Business analysis techniques
Data analysis skills

Requirements gathering and documentation
Communication and presentation skills

The responsibilities of a business analyst include the following:

Identifying business needs and requirements
Analyzing data to understand business trends
Developing data-driven recommendations

Working with Data Scientists and Data Analysts to develop insights
Communicating findings to stakeholders

This is not an extensive list. Different organizations have different titles for their data roles. The job responsibilities may vary slightly depending on the company’s size, industry, and location.

Pick your study resources.

By now, you’d better understand the data science landscape. Based on the responsibilities, you’d have also picked a career option. The next step is to study and acquire the required skills.

There are many resources available online and offline. You can start with a Coursera Specialization, Udacity Nanodegree program, or Harvard’s CS50 course. If you prefer books, “Data Science from Scratch,” “Practical Statistics for Data Scientists,” and “Introduction to Statistical Learning” are some excellent options. There are also boot camps that offer intensive, hands-on training. Especially the Introduction to Statistical Learning’s version with R examples is highly rated by many readers.

That’s overwhelming already. You’ll have to pick the best option before you start learning and stick to it. The most important thing is to keep learning and progressing in your career.

I wouldn’t recommend Youtube playlists at this point. Though some great channels have great resources, you must check their credibility before you dive in. But you will get the best available with established learning partners such as Udacity and Coursera.

Besides, the biggest drawback of Youtube is it’s too distracting. We need to admit it; Youtube is not built with learning in mind. It’s social media. The algorithm is designed to keep you engaged for as long as possible. So, the next thing you know, it’s 3 am, and you’ve just wasted an entire day watching cat videos.

Don’t get me wrong. I’m not against learning from Youtube altogether. But if you want to learn something specific and progress in your career, I recommend other options.

Create a schedule that works for you.

The most significant advantage of self-learning is the freedom to pick your time. But it comes with the responsibility not to over-enjoy it. Could you not take it for granted?

Set aside time every day or week for your learning. It could be in the morning before you start your work or in the evening after you’re done with everything else. The important thing is to have a regular schedule and stick to it.

If you can’t commit to daily learning, spend some time every week studying. It’s essential to have a plan and stick to it.

I recommend starting with an hour or two every day. Once you get into the habit of learning, you can increase the time gradually. The important thing is to make sure you learn something valuable every day and don’t give up easily.

Use a planner to organize your learning. You could create a Monday.com account and use it as your digital planner. Many other options exist, such as Trello, Asana, and Notion.

Learn with peers

It’s easier to stay motivated and on track when you’re learning with peers. And it’s very accurate when learning data science because this field requires collaboration.

Luckily, there are many ways to find peers who want to learn data science. You can join a meetup group or an online forum like Reddit’s r/learnmachinelearning. If you’re enrolled in an online course, take advantage of the student community and forums to interact with other students.

A study from Tampere University found that students appreciate the learning opportunities offered by assignments and activities, which are open and visible to everyone during the course. Yet, another study from George Mason University found that there’s very little interaction with peers due to time and distance constraints.

Hence, knowing these limitations, we must find ways to interact and learn from our peers. But when you do, it’s worth it because you can get insights and perspectives that you wouldn’t have if you were learning independently.

So I recommend joining an online course and taking advantage of the student community and forums. If you’re not enrolled in a class, find a meetup group or an online platform where you can interact with other students.

Conclusion: Take the first step

There’s a lot of ground to cover, and it seems daunting. The most important thing is to take the first step and keep moving forward. Becoming a data scientist is long, but it’s an exciting journey. And the rewards are worth it!

In this article, we’ve explored the different data science career options, their responsibilities, and skills, how to pick the right study resources, how to create a learning schedule that works for you.

Now it’s your turn. Which data science career option are you going to choose? And what is your learning plan?

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Why a Personal Curriculum is Important for Data Science Students