TikTok has remained among the top downloaded apps according to Statista. In 2022 TikTok recorded a stunning 672 million downloads. TikTok videos transformed the social media landscape by introducing short dances and unconventional Gen Z commentaries. The platform’s widespread popularity reflects key technological shifts over the last decade, including the transition from desktop to mobile, the preference for short-form videos among users, and the implementation of innovative advertising strategies.

So, people ask this question “How does TikTok know me so well?” and the answer from our perspective has always been the Data Science team at TikTok has nailed it by building a recommender system that works so well. Let’s see how Data Science fuels TikTok’s success.

What is the TikTok Algorithm?

The TikTok algorithm is a sophisticated system that employs machine learning and artificial intelligence to analyze user behavior and deliver personalized content recommendations. The TikTok algorithm is the brain behind the For You page (FYP), the curated feed of videos that greets you when you open the app. Unlike traditional social media feeds that mostly show you content from people you follow, the FYP is a personalized mix of videos from both creators you know and those you don’t, all chosen by an algorithm to keep you hooked.

How Does the TikTok Algorithm(Recommendation system)Work?

The exact workings of the algorithm are proprietary. However, the general principles of how it works are based on user experiences and insights provided by the platform. To give you more context recommender systems are built on a user-item matrix, this ideally is a table that has rows and columns. The rows represent users and the columns represent individual items, like on YouTube this would represent videos, on Spotify this would be songs.

Now the value in each cell would represent the interaction between the user and the Item, for example, Likes, watch time(did you finish the video), comments, and on platforms like Amazon this could include reviews and star ratings.

With this table, we can create users with similar interests and recommend songs, videos, products, etc based on what other users interacted with. Now back to TikTok …..

Machine Learning for Personalization:

TikTok’s machine learning algorithms process vast amounts of user data, including videos watched, engagement history, and interactions. These algorithms use this data to create a personalized profile for each user.

Machine learning models then predict which videos a user is likely to enjoy based on their historical behavior. This enables the system to continuously refine its understanding of user preferences.

Diverse Content Recommendations:

TikTok’s recommendation system aims to provide users with a diverse range of content to keep the For You Page engaging and varied.

This diversity includes popular trends, niche interests, and content from both well-established and emerging creators, ensuring that users encounter a mix of content that aligns with their preferences and introduces them to new trends.

Engagement Metrics:

TikTok places significant importance on engagement metrics such as likes, shares, comments, and watch time. Videos that receive higher engagement metrics are considered more interesting and are likely to be recommended to a wider audience. This emphasis on engagement ensures that the content suggested is more likely to resonate with users.

Real-Time Feedback Loop:

The recommendation system operates in real-time, continuously adjusting content suggestions based on immediate user feedback. If a user engages positively with a video, the system takes this feedback into account to refine subsequent recommendations. Similarly, if a user quickly scrolls past or skips a video, the algorithm adapts to avoid similar content in the future

Continuous Learning:

The TikTok recommendation system is designed for continuous learning. As user behavior evolves and new trends emerge, the algorithm adapts to ensure that content suggestions remain relevant and engaging over time. Continuous learning involves regularly updating machine learning models to reflect changes in user preferences and the evolving landscape of content on the platform.

Are you the next Data Scientist who will take TikTok to the next level?

Tech has enabled the onset and growth of careers in very unconventional domains. You can be the one earning a salary of $150k or more annually. Kickstart your journey into the highest-paying jobs of our generation by studying Data Science at Moringa. Send an email to admissions@moringaschool.com for more details.

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Data science continues to rise as one of the most in-demand career paths in technology today. Data scientists are essential now in a wide array of industries, spanning from government entities to retail and healthcare. They play a crucial role in organizing and analyzing raw data sourced from diverse channels. This enables enterprises to make well-informed decisions, ensuring operational efficiency, increasing profitability, and fostering overall growth.

What is data science?

Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines techniques from statistics, mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets.

The career trajectory of a Data Scientist can be viewed from four primary dimensions: data | engineering | business | product. The Data Scientist role is characterized by its multidisciplinary nature, and within each domain, the career progression tends to lean towards specific disciplines.

Skills required in Data Science

Professionals in data science require a combination of technical, analytical, and domain-specific skills. Here are some key skills required in data science:

Programming Languages: Proficiency in languages such as Python or R is essential for data manipulation, analysis, and model development.

Statistical Analysis and Mathematics: Strong foundational knowledge in statistics and mathematics is crucial for designing experiments, making inferences, and building predictive models.

Data Cleaning and Preprocessing: Ability to clean and preprocess raw data, handle missing values, and outliers, and transform data into a suitable format for analysis.

Data Visualization skills: Proficient in data visualization tools such as Matplotlib, Seaborn, or ggplot for creating clear and insightful visualizations to communicate findings effectively.

Machine Learning skills: Understanding and application of machine learning algorithms for classification, regression, clustering, and recommendation systems.

Big Data Technologies: Familiarity with big data technologies such as Apache Hadoop, Spark, and distributed computing frameworks for handling large datasets.

Data Engineering: Knowledge of data engineering concepts, including data pipelines, ETL (Extract, Transform, Load) processes, and database management.

Database Management: Proficiency in working with databases, both SQL and NoSQL, for data retrieval, storage, and manipulation.

Domain Knowledge: Understanding the specific industry or domain in which data science is applied, allows for more effective interpretation of results and business impact.

Communication Skills: Strong communication skills, including the ability to explain complex technical concepts to non-technical stakeholders and present findings clearly and understandably.

Problem-Solving Skills: Analytical and critical thinking skills to approach problems, formulate hypotheses, and devise effective solutions.

Collaboration and Teamwork: Ability to work effectively in a team, collaborating with other data scientists, analysts, and domain experts.

Version Control: Familiarity with version control systems, such as Git, for tracking changes in code and collaborating with team members.

Continuous Learning: Given the evolving nature of technology, a commitment to continuous learning and staying updated on the latest advancements in data science.

Data Science Job Outlook

The outlook for data science jobs continues to be highly positive. The demand for data scientists has been steadily increasing, driven by the growing recognition of the importance of data-driven decision-making across various industries.

In 2020, IBM forecasted an anticipated 2.7 million available positions within the field of data science and related professions. Furthermore, they projected a substantial 39% increase in employer demand for individuals specializing in roles such as data scientists and data engineers.

Why Choose a Career in Data Science?

In today’s data-centric world, businesses seek valuable insights from the vast amounts of data generated daily. This heightened demand for data scientists has created diverse career opportunities, ranging from junior roles to director-level positions. If you aspire to enter the field and advance from an entry-level position to a director, understanding the data scientist career trajectory is essential.

Data Science Career Pathways+Trajectory

Associate/Junior Data Scientist

As a Junior/Associate data scientist, your role involves testing new ideas, debugging, and refining existing models. Being a team player is crucial as you contribute ideas, take responsibility for code quality, and make a positive impact. If you aspire to pursue a data science career, starting before graduation is advantageous. Develop proficiency in programming languages like Python, Java, R, and SQL/MySQL, and strengthen your foundation in Applied Mathematics and Statistics. Exposure to the field early on can help determine your interest and fit. Ideal subjects for study include Computer Science, Information Technology, Mathematics, Statistics, and Data Science. Key skills include data science, machine learning, Python, R, research, SQL, data analysis, analytical skills, teamwork, and communication.

Data Scientist’s Mid-Level-I Roles

After gaining one to three years of work experience, you can progress to Senior Data Scientist or specialize in Machine Learning and AI Engineering. Certification becomes valuable at this stage, with organizations favoring certified data scientists. Consider earning relevant data science certifications to enhance your credentials.

Senior Data Scientist and AI/Machine Learning Engineer

As a Senior Data Scientist, your responsibilities include building well-architected products, revisiting high-performing systems, and mentoring junior associates. A Master’s degree and possibly a Ph.D. are common qualifications at this level. AI/Machine Learning Engineers focus on designing, creating, and deploying end-to-end machine learning solutions. Skills required encompass Artificial Intelligence, Deep Learning, Machine Learning, Natural Language Processing, Data Science, Python, C++, SQL, Java, and software engineering.

Principal Data Scientist

With 5+ years of experience, the Principal Data Scientist is highly experienced and well-versed in data science models. They work on high-impact business projects, possess a Ph.D., and often hold a principal data scientist certification. Their role involves understanding challenges in multiple business domains, discovering new opportunities, and demonstrating leadership excellence in data science methodologies. Principal Data Scientists play a significant role in developing junior team members, acting as technical consultants, and contributing to data science projects.

Data Science Manager/Architect

This role requires a combination of knowledge of database systems and programming languages. Responsibilities include team leadership, setting priorities, and communicating findings to management. Certifications such as Microsoft Certified Professional, Certified Analytics Professional, or SAS/SQL Certified Practitioner are common. A Master’s in business administration is recommended due to the emphasis on team leadership and project management.

Data Scientists Advanced-Level Roles

At this level, professionals demonstrate the ability to guide teams, oversee strategic data analysis, and stay abreast of the latest technologies. Leading an organization’s entire data science operations requires skills that can influence the organization’s success or failure.

Key Takeaways

Navigating the data scientist career path is challenging yet rewarding. As you progress, continuously evaluate and enhance your skills, daring to make data work for you and your organization.

Secure the future of your career by joining Moringa’s Data Science Course. Learn in-demand Data Skills that are reshaping businesses and industries today >> APPLY HERE

Article by – Steve Biko | Follow https://medium.com/@stevierbiko for more.

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As The Economist aptly put it in 2017, the world’s most valuable resource is no longer oil, but data. Technology advances have made it much easier to amass data in huge quantities. Capitalizing on this data helps to create value and growth, which is why organizations are investing in people and technological capabilities to extract greater value from data. The digital and data revolution provides massive opportunities for finance functions and accounting professionals as you will understand in Stephen’s story.

Tell us about yourself.

I am Stephen Gathai, a finance professional with over 10 years of experience. I’m also a Certified Professional Accountant (CPA-K) and Certified Investment and Financial Analyst (CIFA), currently working in audit. At Moringa, I am a Data Science Part-time student and we’re in Phase 2, tackling statistics.

You work in audit and have a background in finance. What made you decide to study Data Science?

In the rapidly evolving landscape of technology, the contribution of data scientists plays a pivotal role in driving innovations and success. Currently, auditing has shifted from being qualitative to quantitative, where the use of data is vast. I work in the manufacturing industry where there is a lot of data to sift through. All the data is to be audited and presented with well-backed support. That gave me the drive to study Data Science since before, I could use Excel to conduct my analysis which is time-consuming and inefficient. Learning science-based tools as I am in Data Science enables me to undertake my work with efficiency. With the skills I am gaining, I can comfortably do data elimination, increase my sample sizes and work with huge sets of data with ease.

I believe that you did research before settling on Moringa for the Data Science program. So what made you choose Moringa over other institutions?

I was certain that I wanted to pursue Data Science because of how it would benefit my career. I searched online and checked professional social networking sites such as Linkedin and what intrigued me was that Moringa stood out. The Data Science professionals that I follow on Linkedin have a background in Moringa hence my interest. I reached out to them and they gave me the confidence that joining Moringa would be a stepping stone for my career. This far, I believe that I made the right decision.

Have you worked on any project now that you are in phase 2? If you have, what has been the highlight of the project?

I have, in phases 0 and 1, I learned Data Engineering, Data Visualization with Python – things I did not know. I have been able to apply the knowledge learned at work and the advantage I have is that we are not limited to the tools we can use. In matters of data at work, I would previously liaise with our IT team which would take time to query and analyze data, and would sometimes be manipulated. I recently learned SQL that has enabled me to enjoy aspects of my work that involve querying data to figure out what it means, and talking to people way up in the organization and telling them what the data says the smart decisions are.

I’m thrilled that I can also do Data Visualization which can be understood by people with non-technical backgrounds. I am eager to learn more about data collection, data mining, and forecasting – skills that will come in handy at work.

What are some career aspirations you have once you graduate?

Allow me to paint a picture of our class. We are 84 and as the class representative, I interact with most students. Throughout our group discussions, the classmates that work in statistics pose real-life questions that require us to apply the knowledge learned in class. We thrive on a collaborative spirit when we work together to solve complex problems as we apply the knowledge we have gained to real-life situations. Doing this has broadened my view on the career choices I can make with my skills. I’m halfway into the course in Phase 2, and I believe that by the time I’m in Phase 5, I will be an exceptional Data Scientist.

Once I graduate, I will consider venturing into data-driven audit consultancy. Career options abound.

How would you describe your learning experience at Moringa?

Having gone through 8-4-4 and now as a professional, I realize that a lot of knowledge taught to us back then is irrelevant and was to be utilized in passing exams – as the content lacked a practical aspect. The organization of classes at Moringa is impressive. There are weekly lectures, stand-ups, streamlined communication channels between the Technical Mentors (TMs) and students through Slack, and peer breakout sessions where we have our blockers solved.

Classes start with the TMs inquiring how people are fairing and depending on the responses, they assess how best to conduct the class. There are also feedback sessions where there the TMs incorporate the feedback received into their teaching and classroom interaction with the students. 

Knowing that we are listened to is motivating and gives me the oomph to power through. I’m a family man who is also a working professional, so despite the challenging course, I am determined to excel in my studies because I know that the knowledge I’m learning is equipping me into a well-rounded finance and data science professional. 

What would you tell someone who would like to pursue a career in Data Science?

I would tell them to first enroll at Moringa and secondly, we are in the 21st century and data has become the lifeblood of the digital age. Businesses across industries require people with data analytics skills who are prepared to challenge norms.  An undersold aspect of this field is just how versatile the skills are. Even though a lot of people are gunning for “data scientist” jobs, there are literally hundreds of thousands of jobs right now seeking professionals with data literacy as part of their repertoire. For anyone interested in Data Science, I’d say definitely go for it.

How can people reach you for mentorship or career guidance?

I’m open to mentorship, find me on LinkedIn as Stephen Gathai or send me an email via gathaistephen@gmail.com. I will be happy to connect and offer my guidance.

Interested in upskilling or starting your career in Data Science? Apply now for our next intakes below.

Data Science Full-time classes – https://bit.ly/3CJ8Mv0 

Data Science Part-time classes – https://bit.ly/3CGWF1D 

The post From an Extensive Career in Finance to Data Science – Stephen Gathai’s story appeared first on Moringa School.

Today, more than ever, businesses and governments are becoming more data-driven. This is helped by the fact that, today more than any time in our history, data is more readily available. The amount of data we produce is humongous and is fueled by the accelerated adoption of technology and data-driven tools. The website visual capitalist projects that by 2025 we will be producing about 463 exabytes of data each day! 

With data being more available, and corporations and governments being more data driven, there is an increasing demand for people, employees and business executives to adopt a data mindset. Some of the most demanded skills of the future will be centered around data fluency whether one is a techie or a non-techie. 

Yuval Noah Harari, author of the bestseller, Sapiens, suggests that the rise of data-driven technologies has changed the way we make decisions. The ability to capture, store,synthesize and analyze data has become a critical skill for many organizations today. However, not everyone is comfortable working with data and may find it overwhelming or confusing. The lack of data fluency or data literacy is therefore becoming a big impediment to building competitive advantages either at individual, corporate or government level. 

What is data fluency?

Data fluency is the ability to read, interpret, and communicate data effectively. 

Contrary to the general perception, data fluency is not a skill just for data scientists or analysts but a skill that anyone can and should aspire to. With the ability to work with data, one can understand the story behind numbers, statistics and make more informed decisions based on evidence. 

There are so many benefits that one would get from being more intentional with data. As we are constantly making decisions in and around our environment every single day, a data fluent person will see the quality of their decisions increasing over time. The idea here is not to go all out and start on a journey of being a data nerd but it’s more about always trying to identify patterns from everyday circumstances and building from that.

But how does one become more data fluent?

Tips to becoming more data fluent

As it is with any new skill or craft, becoming a data fluent individual will take time and practice to perfect. The secret source is to adopt an incremental learning mindset and focus on learning at least one new thing everyday. The following are some tips you can adopt as you start on becoming a data-driven individual:

Start with the basics: Always start with the basics! Many people want to jump into the more complex and nerdy stuff so they can, perhaps, show off their skills to their friends and associates. However, it always pays to start small, simple and with the boring stuff. Some key concepts that one can cover at the basic level include learning more about data types, data sources and data analysis techniques.

Experience is the best teacher: Nothing beats experience and practice! Theory is always great, but hands-on experience will convert your data fluency engine from a honda civic to a ferrari in no time. The best way to practice is to try working with real data sets to run basic analytics. Freely available tools like google sheets, data studio, microsoft excel and tableau can get you up and running in no time.

Get a mentor or coach: The role of a mentor or a coach can make the difference between what you learn and how you apply it in context. Mentors and coaches can help us see what is not obvious. They can also help us unlearn ideas and routines that are not useful to the application of a data mindset.Joining  communities of data enthusiasts and online forums can accelerate your learning and help you navigate the tough terrain. 

Tell your stories with data: One of the things data nerds tend to forget is that what makes sense to them may not be so obvious to the next person. Data visualization can help you tell better stories with data. Your goal is to make the data speak for itself. As you become better, your aim should move you towards making your presentations concise and leverage data narratives, storytelling and visualizations to help the rest understand the stories behind your numbers.

What next?

If you want to learn more about data, there are many places you can go to in order to get started. Institutions like Moringa School have great programs for those who want to get started on data fluency.

By developing your data fluency, you’ll be able to make better decisions, solve problems more effectively, and communicate your ideas more persuasively. Data isn’t just for techies; it’s for everyone!

Allan Ong’ang’a
Director-Business Development
Moringa

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