What is Data Science?
Data science is a field of study that centers around analyzing data to get meaningful insight from the data.
The insights gotten are used to support making better decisions in business, health, lifestyle, and every area of human processes.
Data science is the merge of two old fields, Statistics and Computer Science. So, you can think of data science as the field
where statistical principles are applied to data to get an understanding of what the data is saying. These data are historical
data (past data) on a particular subject of interest. It could be data from individual income spending, bank transactions, medical records,
employee data, sales data, marketing data, data from a sports activity, or traffic report data. Any type of data can be analyzed by implementing statistical principles, functions, and methods to get meaningful and applicable insight from the data.
the role of computer science in data science is the provision of new tools that help statisticians process faster than and better than they do before.
Growth in data over the past decades necessitated additional computing power, which in turn spurred the development of statistical methods to analyze large datasets.
These two fields have now created the data science field which requires but statistical skills and programming skills.
Experts in this new field are known as Data scientists.
So, what does Data Scientist do?
Data scientist are responsible for everything that happens to data, from collecting the data to cleaning the data or preparing
the data for analysis to analyzing the data and creating a report of their findings of insights gotten from the data.
Data Science lifecycle involves 5 processes:
1. Data Collection or Gathering: gathering data is a crucial part of data science. Data can be gathered from different sources such as through surveys (Offline and Online), questionnaires, from a company database, scraping data from websites, accessing data
from APIs, and so on. the list of data sources is endless.
2. Data Management: managing data involves storing the data, cleaning the data, preprocessing the data for analysis, and so on.
3. Exploratory Analysis: the data collected is to be understood before major questioning can be done on it. Exploratory data analysis (EDA)
that can be done includes inferential analysis, Data centrality, Variability, Plotting Charts, Pattern and Trend search, and so on. this will help the data scientist
familiarize with the data and see all the possible deeper analyses that can be done on the data.
4. Deeper Analysis: here is where the data scientist ask the deeper question of the data using more advance statistical and mathematical techniques which
might require a high level of computer programming skills.
5. Reporting: reporting is done by presenting the insights gotten from the data in a simple to understand form which would make it easier for
insights to be understood by stakeholders so that meaningful decisions can be made based on the insights.
Myths about Data Science.
it is widely said that you need a background in Statistics, Mathematics, and Programming skills to become a Data Scientist, but I dare say
that you don’t need any of this. Starting a career in Data Science is similar to the learning process you might have gone through.
The data science field is open to everyone from any background. You can start your learning journey from scratch with no programming skills.
There are several learning resources online that can make your learning process easy and you can do that at your own pace.
Data Science is a growing field and there are several career levels and areas of expertise that you can achieve as you progress in this field. Some of the areas of expertise and career levels are as follows:
1. Data Engineering and Data Warehousing: Data Engineering involves the ability to transform raw data into a useable format for analysis.
This often involves managing the Source, Structure, Volume, Velocity, Veracity, Storage, and Accessibility of the data so that it can be available for analysis at any given time.
Job profiles associated with this expertise are Data Engineer, Data Analyst, and Data Developer.
2. Data Mining: Data mining involves applying statistical principles to perform Exploratory Data Analysis.
it involves having the curiosity to ask the right questions from data that will give the right insight. It
also involves developing data models and also predictive models to reveal patterns and trends in the data.
Job profiles associated with this expertise are Data Scientist, Data Analyst, and Business Analyst.
3. Database Management: Database management involves designing, deploying, and maintaining databases. This involves the warehousing of data for analysis.
Job profiles associated with this expertise are Database Administrator, Database Engineer, and Data Specialist.
4. Business Intelligence: BI involves managing data source for data consistency and accuracy, developing tailored analytics system,
reporting to stakeholders, developing and managing dashboards, implementing analysis results in business processes.
Job profile associated with this expertise is BI Engineer, Data Analyst and Strategist, and BI Developer.
5. Data Visualization: Presenting data in a simple and appealing form is part of the expertise required in the Data Science field.
this involves using BI tools to present data in real-time chart dashboards that convey insight into the data in the simplest way.
Job profiles associated with this expertise are BI expert, Data Analyst, and Data Viz Engineer/Developer.
6. Machine Learning and Cognitive Computing: this is a popular part of the Data Science field. It entails getting a deeper insight from
Data by employing more complex data mining methods, statistical principles, and computer programs to build robust data models (robot).
Cognitive computing makes it possible to build data models that will give more accurate insights using high computing power, statistical principles, and a large dataset. With this, prediction and forecasting become easier.
Job profiles associated with this expertise are ML Engineer, Data Scientist, and AI Specialist, and Cognitive Developer.
Begin your journey to a fulfilling Data Science career by just taking the first step. Evaluate if it is a field you will like to go into. Get yourself into training resources and begin to learn. Find yourself a mentor in this field, someone with experience who can help chart your learning journey.