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The Best Choice Between Data Analytics and Data Science Career

Data Analytics and Data Science Career in Kenya has taken most professionals by storm. For people looking for long-term career potential, data analytics and data science jobs have long been a safe bet. This trend is likely to continue over the years as AI and Machine Learning become highly integrated into our daily lives and economy.

In Kenya today, data is the new oil for businesses to gather critical insights and improve business performance to grow in the market. But who will glean insights? Who will process all the collated raw data? Everything is done either by a data analyst or a data scientist.

These are the two most popular job roles in this area as companies across Kenya look to make the most out of data. Data Science and Data Analytics is a mishmash of terms which interweave and overlap with one another but are still quite different.

Many professionals in Kenya getting started with a career in data analytics or data science are often confused and unsure about which is the right career path to take . Well, the decision would depend on what your career goals are, as well as your skills and competencies. That said, to spare you from any kind of confusion and provide you a crystal clear insight into these two innovative career paths, here we explore Data Analysis and Data Science.

This article aims to help you better understand the difference between the two disciplines so that you can make a decision as to which career path would better suit your career aspirations.

Data Science vs Data Analytics — What's the Difference?

Data analytics career focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modeling. Data science is a multi-disciplinary blend that involves algorithm development, data inference, and predictive modeling to solve analytically complex business problems. On the other hand, data analytics involves a few different branches of broader statistics and analysis.

Data Science vs Data Analytics — The Skills and Tools

Top data analyst skills include data mining/data warehouse, data modeling, R or SAS, SQL, statistical analysis, database management & reporting, and data analysis. For data science, these include machine learning, software development, Hadoop, Java, data mining/data warehouse, data analysis, python, and object-oriented programming.

Data Analytics — Knowledge of Intermediate Statistics and excellent problem-solving skills along with….

  • Proficiency in Excel and SQL database to slice and dice data.
  • Experience working with BI tools like Power BI for reporting
  • Knowledge of Stats tools like Python, R or SAS

To become a data analyst, one need not necessarily hail from an engineering background but having strong skills in statistics, databases, modeling, and predictive analytics comes as an added advantage.

Data Science — Math, Advanced Statistics, Predictive Modelling, Machine Learning, Programming along with –

  • Proficiency in using big data tools like Hadoop and Spark
  • Expertise in SQL and NoSQL databases like Cassandra and MongoDB
  • Experience with data visualization tools like QlikView, D3.js, and Tableau.
  • Proficiency in programming languages like Python, R, and Scala.

Data Analyst vs Data Scientist — Roles and Responsibilities

Data analysts are often responsible for designing and maintaining data systems and databases, using statistical tools to interpret data sets, and preparing reports that effectively communicate trends, patterns, and predictions based on relevant findings.

Data scientists are typically tasked with designing data modeling processes, as well as creating algorithms and predictive models to extract the information needed by an organization to solve complex problems.

Data Analyst Job Roles involves –

  • Exploratory data analysis
  • Data Cleansing
  • Discover new patterns using various statistical tools.
  • Develop visualizations and KPI’s

Data Scientist Job Role involves

  • Processing, cleansing and verifying the integrity of data.
  • Exploratory Data Analysis
  • Gleaning business insights using machine learning techniques and algorithms.
  • Identifying new trends in data to make predictions for the future.

Data Science vs Data Analytics — Which one should I choose?

At learnerscoach, we have partnered with the best institutions in Kenya that offers Data Analytics and Data Science courses with the help of industry professionals to guide aspiring professionals to pursue lucrative careers in the big data world.

To more effectively understand the differences between Data Analytics and Data Science course, we suggest individuals consider some of the important dimensions like the tools and technologies that can be mastered in each of these courses. Having a practical hands-on working knowledge and expertise of various analytical and database tools is the secret success mantra to excel in Data science and analytics industry.

Conclusion

Data analysis involves answering questions generated for better business decision making. It uses existing information to uncover actionable data.

Data analytics focuses on specific areas with specific goals. On the other hand, data science focuses on discovering new questions that you might not have realized needed answering to drive innovation.

Unlike data analytics which involves checking a hypothesis, data science tries to build connections and shapes the questions to answer them for the future.

If data science is a home for all the methods and tools, data analytics is a small room in that house. Data analytics is more specific and concentrated than data science.

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