Data Science vs. Data Analytics question and what to choose between the two data fields is such a common question.
Data is the new currency, so they say. In a data-driven world like we are in now, most organizations, if not all, highly rely on data to decide profoundly on crucial matters that affect their operations.
The entire process of gathering information, processing, and analyzing it to produce valuable processed data creates different job opportunities for people with relevant data manipulation skills. In this case, Data Science and Data Analytics are the two primary job professions dealing with the processing and analysis of data.
What exactly do a Data Science career and Data Analytics career entail? Do they mean the same? How different are they? What preliminary skills would one require to pursue either of these professions as a career?
By answering these questions, this article will help you decide what path to follow in a career between Data Analytics and Data Science. I know you are excited as I am!
Let us look at the major components you consider before deciding on a path between the two.
These components include:
- Data Science job description vs. Data Analytics job description.
- Individual responsibilities in Data Analytics and Data Science.
- Skills required in Data Science vs. skills required in Data Analytics.
- Education background.
- Your interest is in line with these fields.
Job description
Data Science
Data science professionals fetch data from various sources within an industry. They then incorporate computer science, predictive analytics, statistics, and machine learning to parse these vast data sets. This helps them understand the business performance and their functional structure and find solutions to their requirements.
Data scientists unearth questions you do not realize; they require answers in the first place. This aims to identify a potential platform of study or, even better, innovation!
Data Analytics
This field involves processing and statistically analyzing existing information to bring to light actionable data. Data analysts develop techniques to collect, process, and organize data to understand the problems in question.
The experts use data analytics tools like; Python, R, Microsoft Excel, and Tableau to achieve their goals. Additionally, they scrutinize data for accuracy and quality. They further analyze this data before they develop models to report the data and present it to the target audience. The presentation method using models allows the audience to quickly understand the data presented and let them make decisions easily.
Defined responsibilities
The responsibilities of a data scientist
A data scientist cleans, processes, and manipulates the raw fetched data. They use several data analytics tools to gain valuable insights from the data. Data science experts design data modeling processes that develop algorithms and predictive models to perform custom analysis.
Additionally, data science involves the mining of data and collection of large sets of both structured and unstructured data sets from numerous relevant sources. As a data scientist, you will be responsible for designing and evaluating advanced statistical models to interpret this data. Moreover, building predictable models and Machine Learning algorithms that work on large data volumes is also part of a data scientist’s responsibilities.
The processed data needs to be analyzed and then graphically represented in dashboards and reports using charts and graphs. The visual representation helps relevant stakeholders quickly understand data trends and patterns before making decisions. This process is called data visualization. Data Scientists often work with data engineers and data analysts whenever necessary to formulate an analysis of the data results.
A data analyst’s responsibilities
Responsibilities in the data analytics field are not far different from those in data science.
They use relevant data to prepare reports that depict precise patterns and trends. A data analyst needs to understand the company’s role and requirements so they can streamline the entire data analysis process. They also use automated tools to fetch these requirements from primary and secondary data sources.
After collecting the data, they process it by getting rid of corrupted data, debugging code errors, and any related problems.
Data analysts also analyze data to assess its quality and meaning.
Additionally, they develop and maintain databases to reorganize data in readable formats.
The data analysts use statistical tools to diagnose and make predictions by identifying, analyzing, and interpreting patterns and trends in complex data sets.
These professionals assign a numerical value to critical business functions to assess and compare the business performance over time.
Like data scientists, they also collaborate with programmers, engineers, and management heads to continue identifying opportunities for process improvement.
Now, let us look at the crucial skills both fields require.
Skills required
Data Science
This field requires you to be well knowledgeable in Mathematics, advanced statistics, predictive modeling, Machine learning, and of course, being conversant in programming skills, including :
- SQL and NoSQL databases, including MongoDB and Cassandra.
- Being conversant in working with data visualization tools like Tableau, ChartBlocks, Datawrapper, D3.js, and QlikeView, among others.
- Expertise in programming languages like R, Python, and Scala.
- You should be proficient in using Hadoop and Spark, big data tools.
Data Analytics
Equally important, a data analyst needs to know Statistics, databases, modeling, and predictive analysis well. Furthermore, it would help if you were a problem solver and have skills like:
- Excel and designing and developing SQL databases to manipulate the data.
- Using Business Intelligence (BI) tools like Power BI that report the data.
- Programming languages like Python and R for statistics.
- Mastery in Mathematics to collect, measure, organize and analyze data.
- Expertise in using reporting packages like programming, including JavaScript, XML, or ETL frameworks.
Education Background
Beyond the technical expertise, a bachelor’s degree in a relevant statistical and analytical skills field or computer science will be helpful for a data analyst. Alternatively, complete a data science certification course or a suitable Bootcamp. Working on personal projects is an added advantage that will help you become proficient enough for a job role.
On the other hand, a data scientist requires you to have advanced degrees or masters in mathematics or computer science courses.
Interests
Do you enjoy working on mathematical computing or systems thinking? Generally, do you love to create systems that consume data? Data science would suit you best. This is because data scientists mainly build systems for data analysis and use machine learning skills to create predictive analytic models. Additionally, they also design the company’s data collection system and use a computing perspective to define processes.
Otherwise, if you enjoy working towards achieving goals by extensively exploring data and coming up with patterns or trends from this data, Data analytics is a perfect pick for you.
Professional rank 🕵️♂️
Compared with Data Analytics, Data Science has a higher professional rank. Similarly, salary-wise, data scientists earn relatively higher than data analysts. In the US, their average salaries are $110,000 and $65, 300 respectively. Lucrative field, right?😊
However, more opportunity vacancies are available for data analysts than data scientists.
Therefore, what would it be for you? 🤔
Having gone through Data Science and Data Analytics differences, you are in a better position to decide between the two now.
Probably you will want to reconsider your main career goals, interests, and, more importantly, skills.
Whatever path you choose, both fields are advantageous. Moreover, you will never go wrong working with data despite your career choice.
You can explore more about the Data Analytics courses available if you opt for this path. Alternatively, you may want to know the Data Science tools you will be using, should you opt to go the Data Science way.
Best of luck!
