I am not going to brag. It is not the ultimate or complete roadmap. If you had a dollar for every “complete roadmap” article churned out online, acquiring data science skills would be at the bottom of your priority list in your newly-found wealth status.
It is a summarized personal view on what you can do to learn data science — the 21st-century fossil driving every business decision, competitive edges and business model of current and up-and-coming corporations.
Once crowned as the “sexiest job of the 21st century” by Harvard Business Review, data science is the heartbeat of every living organism recognized as a modern business by all humans. It is the motor engine that powers giants such as Amazon, Walmart, Google and Facebook.
A quick Google/Bing search of the term “data science” will flood your screen with gazillions of articles, blog posts, video content, and courses offered by edtechs such as #Coursera and traditional tertiary learning institutions like Harvard and MIT. It is easy to get confused with the tonne of information out there describing how you can be a data scientist. I am going to give my simplified version of how you can start your journey as a data scientist.
The key concepts under review include:
- What is Data Science?
- Why is Data Science Important?
- Who is a Data Scientist?
- Data Science Skills and Tools
- What Next?
1) What is Data Science?
Data science is a multidisciplinary field that uses scientific methods, processes, statistics, algorithms, and systems to extract knowledge and insights from data. The data can be structured(organized in a tabular format like spreadsheets where each observation of a variable is stirred as rows and columns) and unstructured data(data that is not tabular in nature; videos, images and audio recordings).
In simpler terms, data science entails the study of data to extract meaningful and relevant insights for business and non-commercial benefit.
The scope of data science encompasses different types of analytics performed during the data science lifecycle. Types of analytics adopted in the data science process include:
Descriptive Analytics: Examines historical data to understand current performance and identify trends. A good example is an Airbnb host analyzing data of previous customer bookings. Descriptive analysis will illustrate best performing months and seasons where the number of bookings surge or decline.
Diagnostic Analysis: Detailed analysis of data to understand the root causes of events, patterns and anomalies. Diagnostic analysis is characterized by data discovery, data mining, and correlations. A good example would involve an Airbnb host drilling down on why the number of bookings in a given month slumped.
Predictive Analytics: It involves forecasting future events/actions based on past data linked to the event. Predictive analytics is characterized by machine learning, forecasting, pattern matching, and predictive modeling. For example, the Airbnb host can utilize past booking data to forecast the number of bookings for the subsequent year.
Prescriptive Analytics: Provision of viable recommendations based on results of the other analytics. The potential implications of different choices are analyzed before an optimal choice is selected. A good example is a navigation app advising the fastest route based on current traffic conditions.
2) Why is Data Science Important?
The benefits of adopting data science in business processes are enormous. Over the years, the use cases of data science in business have evolved in various facets of a business such as operations, finance, marketing and human resource management.
Depending on the business objective and business challenge, corporations can leverage internal data and public data in crafting viable solutions that yield long-term benefits for stakeholders.
Data science strategies can help businesses and not-for-profit entities in the following ways:
- Improving business processes: Optimal resource allocation, evaluating performance and process automation are some of the business processes that can be improved by using data science processes and systems. Common business use cases that incorporate data science processes include customer churn prediction, customer segmentation, fraud detection and price optimization. A logistics company can use data science to optimize its supply chain network efficiencies via route optimization, forecasting demand and warehouse management.
- Discovering new insights and patterns: Data science can help ventures discover new insights that were buried in internal company data. Discovered insights strengthen a company’s competitive edge, thus improving the quality of customer experience and products sold to clients.
- Develop new innovative products: Data science can reveal gaps and problems that can be commercialized via the creation of new products and solutions. Customer preferences and trends can enable businesses to create tailor-made products that provide a premium customer experience. A good example is Netflix's use of customer data in producing binge-worthy TV shows(#Squid Game, #House of Cards).
3) Who is a Data Scientist?
Based on a five-year-old child's perspective, a data scientist is simply a magician who creates magic out of data(information).
If asked by a peer interested in understanding who is a data scientist, the answer would be that a data scientist is a detective who solves a crime(business problem/societal problem) by questioning data.
In simpler terms, a data scientist solves business problems by understanding and analyzing structured and unstructured data. The data scientist finds a treasure out of messy data.
Data scientists execute the following tasks:
- Find patterns and insights hidden in datasets
- Develop algorithms and models that predict outcomes
- Use machine learning frameworks to optimize the quality of data and data-oriented products
- Use a wide range of tools and techniques for preparing and extracting data
- Communicate data findings to business executives
- Collaborate with data analysts, data engineers, business owners, and machine learning experts in executing end-to-end data science projects
It is worth noting that data scientists do not work alone. Collaboration with other data specialists is integral in ensuring business objectives and business problems that rely on data are executed successfully.
4) Data Science Skills and Tools
A qualified data scientist should possess certain knowledge, skills and tools proficiency. Based on the definition of a data scientist, the key competencies needed for one to be recognized as a data scientist can be categorized into technical and non-technical.
Technical skills include:
a) Statistics and Probability
Statistics is the fodder of data science. It is a mathematical discipline dealing with data collection, analysis, interpretation, and organization. Probability deals with quantifying uncertainty. The duo form an integral component of understanding the fundamentals of data science.
Understanding concepts such as distributions, statistical tests and probability theories enables a data scientist to extract meaningful insights from data.
You can have a look at the introductory course on statistics offered by Datacamp: Introduction to Statistics Course.
b) Programming
Programming involves writing scripts/code in a specific language that enables fast execution of commands. Using a programming language to interact with large volumes of data is beneficial and efficient, especially when speed is important in aiding decision-making.
Scripts are written by data scientists using a programming language instructing the computer to implement a specific set of instructions such as data manipulation, statistical analysis, and machine learning.
Programming languages used in programming data science challenges are:
Python: Its simplicity and powerful libraries like pandas and NumPy make it ideal for working with data.
R: Great for statistical analysis and visualization.
The two languages are popular due to their ease of use and powerful data-handling libraries. They are also open-source, meaning they are free to use.
There are dozens of resources you can use to learn Python and R. Try to check out: Python Tutorial and Introduction to R Programming.
c) Data Visualization
Communication of data findings can be achieved via data visualization. Data visualization entails representing complex data in a visual and easily comprehensible format. It involves the use of storytelling techniques to convey ideas and insights to specific audiences.
Visualization tools that can be used to visualize data include:
Matplotlib and seaborn: Python visualization libraries
Power BI: Microsoft’s Business Intelligence Tool
Tableau: Used for creating interactive visuals
d) Machine Learning
Machine learning is a subset of artificial intelligence that involves the use of mathematical models of data to help a computer learn without direct instruction. It is the bedrock of many modern data science applications such as recommendation systems used by Netflix and Spotify.
Understanding how machine learning algorithms work under the hood enables a data scientist to improve the quality of data thus optimizing the outcomes of predictions made by the machine learning algorithms.
Common machine learning libraries used by data scientists in their day-to-day work include:
Scikit-learn: Provides various algorithms for classification, regression, clustering, etc.
TensorFlow: Used for building neural networks.
PyTorch: Known for its dynamic computation graph.
If you are interested in understanding the basics of machine learning, check out this course >>>: Understanding Machine Learning Course
e) Data Engineering/Database Management Systems
Data engineering deals with the creation of systems for collecting, storing, and processing data.
Databases are the building blocks of data engineering. Databases rank among the most common solutions for data storage. SQL is the language used to interact with databases.
Key concepts one can learn:
Relational Databases: Use SQL for database management
Non-relational databases: Use NoSQL databases like MongoDB
Database design
Data Modelling
If you are interested in understanding how to interact with relational databases, check out the SQL course >>>: Introduction to SQL Course
5) What Next?
Becoming a qualified data scientist requires training. There are various steps you can take:
Get a data science degree from a reputable institution. Acquiring an academic credential will show you are capable of handling a data science project/job.
Learn from free online sources.
Acquire data science certificates from reputable edtech platforms like Coursera, DataCamp and Codecademy
Congratulations!!!!. You made it to the end of the article. Grab a glass of juice, stand outside or just listen to your Podcast playlist as you plan your data science journey today.
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