Getting Started with Data Science

7 min readJan 10, 2023

What is Data Science

Data science is a process, not an event. It is the process of using data to understand different things, to understand the world.

For me, it’s when you have a model or hypothesis of a problem, and you try to validate that hypothesis or model with your data.

Data Science is the art of uncovering the insights and trends that are hiding behind data. It’s when you translate a data into a story. so use story telling to generate insight. And with with this insights, you can make strategic choices for a company or an institution. Data science is a field about processes and systems to extract data from various forms of whether it is unstructured or structured form.

Data Science is the study of data. Like biological Sciences is the study of biology, Physical sciences is the study of Physical reactions. Data is real, data has real properties, and we need to study them if we are going to work with them. Data science involves data and some science.

Data Science is the process and method for extracting knowlege and insights from large volumes of disparate data. it’s an interdisciplinary field involving mathematics, statistical analysis, data visualization, machine learning, and more.

In summary, Data Science is the field of exploring, manipulating, and analyzing data, and using data to answer questions or make recommendations.

Data Scientists use data analysis to add to the knowledge of the organization by investigating data, exploring the best way to use it to provide value to the business.

Qualities of a good Data Scientist

Curiosity: curiosity is absolute must; if you are not curious, you won’t know what to do with data at your disposal. Good data scientists are inquisitive individuals who probe the business need. “What information do we need to address the problem, and where will it come from?” are the next questions.

Judgmental: you wouldn’t know where to begin if you don’t have a preconceived notions about things.

Argumentative: If you can make an argument or make a case, at least you can start someplace. Then, you may adjust your assumptions and hypotheses based on the data and learn more.

Technical skills: comfort and flexibility with some software, computing platforms and analytics platforms I will write more on the technical skills every data scientist require later in this article.

Ability to tell a story: this is the big gig amongst the others, you must be able to tell a great story out of your data; you must be able to communicate results to people after your analysis. if you can’t tell a great story from your data, your findings will remain hidden, remain buried, and nobody will notice it.

Fields where Data Science is applicable

Data Science is applied in different ways across; Healthcare, Banking & Finance, Entertainment, Manufacturing, Retail marketing, IT industry, Oil & Gas, E-commerce, Transportation, Education, Spots, etc in the following ways;

Healthcare: Construction of advanced medical tools to diagnose and treat diseases is being done by healthcare corporations employing data science.

2. Gaming: With the use of data science, video and computer games are currently being developed, which has elevated the gaming experience.

3. Fraud Detection: Data science and related algorithms are used by banking and financial institutions to identify fraudulent activities.

4. Internet search: Google comes to mind as soon as we think of search. Right? Other search engines, like Yahoo, Duckduckgo, Bing, AOL, Ask, and others, use data science algorithms to quickly provide the most relevant results for our search query. Considering that Google processes over 20 petabytes of data each day. If data science did not exist, Google would not be what it is today.

5. Logistics: Logistics organizations utilize data science to optimize routes to ensure faster product delivery and boost operational effectiveness.

6. Image Recognition: One of the most common data science applications is the detection of objects in photos and the identification of patterns in images.

7. Recommendation systems: Based on what you prefer to watch, buy, or explore on their platforms, Netflix and Amazon propose movies and products to you.

8. Targeted advertising: If you thought that Search was the most important application of data science, consider other aspects of digital marketing. Nearly everything can be recognized using data science algorithms, from display banners on various websites to digital billboards at airports. This explains why digital advertising has a far higher CTR than traditional marketing, which is much lower. They can be tailored based on a user’s prior activity. This explains why some people in the same area may see advertisements for clothing while you see advertisements for training programs in data science.

9. Speech recognition: Data science techniques predominate in speech recognition. These algorithms’ excellent work may be evident in our daily activities. Have you ever had a need for a virtual speech assistant like Siri, Alexa, or Google Assistant? Its voice recognition technology is working in the background to try to understand and assess your words and provide you with valuable information based on your use. On social networking sites like Facebook, Instagram, and Twitter, image recognition is also possible. These programs will identify and tag people in your list when you upload a photo of yourself with them.

10. Airline Route Planning: Data science has made it simpler for the airline industry to anticipate flight delays, which is assisting in its expansion. Determining whether to make a stop in between and then land at the destination, such on a flight from Delhi to the United States of America, or to land right away at the destination is also helpful.

11. Augmented reality: Last but not least, the final applications of data science seem to have the most promise for the future. Yes, we are not talking about augmented reality right now. Do you realize that data science and virtual reality have an interesting relationship? For the best viewing experience, a virtual reality headset combines data, algorithms, and computing knowledge. Pokemon GO, a well-known game, is a little step in that direction. the freedom to explore and spy Pokemon on buildings, roads, and other imaginary surfaces. Utilizing information from Ingress, the company’s previous app, the developers of this game selected the locations of the Pokemon and gyms.

Different Fields of Specialization in Data Science

Data Science is a broad name that comprises of different fields of specialization. The different areas of specialization in Data science is listed below;

Data Scientist: Determine the nature of the issue, the questions that need to be addressed, and where to find the relevant data. Additionally, they mine, clean, and present the relevant data.

Required skills: Statistical & mathematical skills, Understanding of Hadoop, SQL, data wrangling, machine learning, story telling, data visualization, and programming (SAS, R, and Python).

2. Data Analyst: A data analyst collects, cleans, and examines data sets to aid in problem-solving. Here’s how to set off on the road to becoming one. A data analyst gathers, purifies, and deciphers data sets in order to provide an answer or find a solution.

Required skills: Statistical and mathematical skills, programming skills (SAS, R, Python), plus experience in data wrangling and data visualization using different tools like Pandas, Numpy, Matplotlib, Scikit-learn, Tableau, etc.

3. Data Architect: Data architects build strategies to integrate and maintain a variety of data sources as they design the framework for data management systems. They are in charge of the infrastructure and underlying operations. Their primary objective is to make it possible for employees to get information when they need it.

Required Skills: Programming languages such as Python and Java, data mining and management, machine learning, SQL, and data modeling.

4. Machine Learning Engineers: As a data scientist or engineer, you can advance to this position, which is not entry-level. Machine learning interprets data and improves accuracy over time by using algorithms that mimic how people learn and behave. Machine learning engineers do research, develop, and design artificial intelligence that supports machine learning as a member of a data science team. They also act as a point of contact for data scientists, data architects, and other professionals.

Required skills: Knowledge of tools such as Spark, Hadoop, R, Apache Kafka, Tensorflow, Google Cloud Machine Learning Engine, and more. It also helps to have a rudimentary knowledge of computer science, quantitative analysis, and data structures and modeling.

5. Data Engineers: Data engineers focus on developing, deploying, managing, and optimizing the organization’s data infrastructure and data pipelines. Engineers support data scientists by helping to transfer and transform data for queries.

Required skills: Python, SQL, MySQL, NoSQL databases (e.g., MongoDB, Cassandra DB), programming languages such as Java and Scala, and frameworks (Apache Hadoop).

Tools for Data Science
Although the field of data science is tough, there are luckily many tools accessible to support data scientists in their work.

Data Analysis: A data analyst makes use od different tools for data analysis, this include; RapidMiner, MATLAB, Excel, SAS, Jupyter Nootbook, and R Studio for data analysis.

Data visualization using Tableau, Cognos, Jupyter, and RAW ;
Informatica/Talend, AWS Redshift for Data Warehousing

Machine Learning: Azure ML studio, Mahout, and Spark MLib

Prerequisites for Data Science