Starting a career path in data science which is one of the most popular fields of tech today, and is predicted to grow further in the next few years but before pursuing a career it's better to understand what data is and the career aspects that it entails. First off, what is data?
Data can be said to be a collection of facts, figures, or details in general about people, places, or things. Data is everywhere around us today from the number of tweets we post a day, to our unique bank accounts number when dealing with this much data generated regularly, there is a need to filter, store and manage them. Data can be classified into 2 types, qualitative and quantitative data.
- Qualitative data is the non-numerical type of data consisting of descriptions and letters like job-title, company names, and so on.
- Quantitative data are data that are numerical in format and can be counted.
Classes of data
Data can be classified into three forms:
1.Unstructured data This type of data which is unarranged, and raw format, these types of data require several pre-processing and tuning to be understood and able to gain insight. These types of data are gotten from data lakes.
2.Semi-structured data Semi-structured data are those which are partially structured but do not have a fixed form, this kind of data may contain missing values but are possible to find relations or structure.
3.Structured data These are data that have been arranged in a relational database format and can be easy to understand, use and draw insight from.
When talking about data science the different data paths are interrelated but quite stand out in certain areas they are:
1. Data engineers- These are the people who focus on data collection and database creation from raw unstructured or semi-structured data. They use their advanced engineering skills to manage a large amount of data gathered using cloud-based software to produce a structured data model.
2. Data analysts-They have the responsibility to be able to draw insights from data given by the data engineers, they organize and visualize the data as well as create dashboards for easy and proper understanding of the data using various tools.
3. Data scientist-They have the responsibility of drawing insights from data and creating prediction models for the target value of the company then presenting and visualizing the model and how it works to improve business and the industry.
4. Machine learning engineers- They focus on creating machine learning models with already cleaned and structured data and utilizing deep learning (neural networks) to create models for the use of the company and deployment.
Skills required when becoming a data scientist.
- Good communication skills- Some working with data cannot handle it alone, most companies hire several data scientists or their services so working on a team requires good collaboration and communication skills.
- Data cleaning- A good data scientist should be able to clean data and clear missing values in data.
- Ability to visualize data rightly- A good day scientist should have good visualization skills, knowing what to visualize and the best way to do so.
- Programming skills- A good data scientist should know how to program or code as it has many uses in drawing insight and building models for data. Required languages to learn include python or /and R programming language, SQL.
- Good storytelling skills-A good data scientist should be able to not only speak technically but also be able to put life into the data through exemplary storytelling skills.
- Good maths and statistics skills-A good knowledge of maths and statistics are needed to understand and draw insight from data.
- Cloud computing-A good data scientist should be familiar with cloud-based services and be able to operate and use them when managing data.
- Version control-Version control with git and GitHub knowledge is required when working in an organization.
- Deployment- A good data scientist should know about a model deployment (with Python:Flask or Django).
A role in the field of data science can be quite tricky because when working with different companies they have different areas of focus and you might be required to do mainly a sub-field that you might not want to but in all its quite an interesting field and growing as well since no matter what, data will always remain and data scientist will be needed. Hope you enjoyed this article, read, like, and comment thanks.