Tue. Aug 2nd, 2022
    Data analysis and data science

    Difference Between Data Analysis and Data Science

    The key difference between Data Analysis and data Science is which branch of big data each field focuses on: while the second one is on the road to discovery with broad views, the first one is more focused on the operations of different companies that apply and seek solutions to existing problems.

    What is data science?

    What is data science? it is a broader field than Data Analysis. It covers different steps from cleaning the data, analysing it, providing a confident prediction for the future and also making it a final valuable product. It include Data Analysis but also decision science, machine learning and data engineering.

    What is data analysis?

    Data Analysis is what helps companies to make the most of informations they have.
    It consists of different steps from cleaning, transforming, visualizing, and extracting the data. And it is super important for companies, because it helps them to make the most accurate decisions.

    So now that you know that the difference between Data Analysis and data Science, you can start learning more about data.

    What are the applications of each discipline between Data Analysis and Data Science?

    Based on this, another major difference between the two disciplines is how they are applied in different industries. In fact, data science has had a huge impact on search engines, which use algorithms to provide better answers to user queries in the shortest possible time. Likewise, data scientists have had a significant impact on the development of recommender systems. For primarily visual content, such as Netflix, or shopping sites such as Amazon, these systems provide customers with much more accurate recommendations, greatly enriching the shopping experience. user.

    In the case of data analytics, it is being used more frequently in sectors such as healthcare, allowing healthcare centers to take care of their patients more effectively.

    This discipline is also frequently used in other sectors such as energy management, since, through data analysis, they can optimize the use of resources and even choose to automate certain services, thus avoiding unnecessary costs. Analysts are also highly sought after by the hospitality industry, as they can help hotels uncover travelers’ preferences and offer them the alternatives that best suit their tastes and needs.

    As you can see, there are many factors to consider before jumping into the world of big data. Data analytics and data science are very closely related disciplines, but are different. So we know it can be difficult to choose which way to go. Here’s a summary of the main differences we’ve talked about throughout this article between Data Analysis and Data Science:

    Data Science

    Data Analysis

    • Creation of predictive models and algorithms
    • Wider and more diversified field of activity
    • Expert in statistics and mathematics
    • Experience with SQL
    • Skilled in Python, R, SAS and Scala
    • Advanced knowledge of machine learning
    • Tendency to work with unstructured data
    • Applications in sectors such as artificial intelligence, health, blockchain or website search engines
    • Draws conclusions from different data sources
    • Field of activity limited to the sector of activity
    • Familiar with data warehouse, ETL tools and business intelligence
    • Strong command of Python and R
    • Data Enrichment Expert
    • Skilled in data visualization
    • Business knowledge and decision-making skills
    • Applications in industries such as retail, travel, healthcare or marketing

    Data Analyst and Data Scientist – Which One Should You Learn?

    Don’t worry – we won’t go too deep into the comparisons. Information is presented as clearly and concisely as possible. With that said, let’s start our data analyst vs data scientist comparison from the very first point – popularity.

    Which is the most popular?

    Popularity can be a difficult point to conclude. However, a great way to look at it may be to simply go to a search engine (Google), type in both job posts, and then compare the results from the first pages that appear.

    Certainly, when it comes to the comparison between “data analyst and data scientist”, data analysis seems to be the most popular one that people are looking for. While there are many reasons for this, the biggest seems to be the fact that some people don’t even know that a “data science” even exists.

    Which is the most difficult?

    Needless to say, data science takes this point without question. Data scientists have the same responsibilities as data analysts – and even some! Since the amount of work and its complexity are higher for IT specialists, it is only natural that their job is all the more difficult compared to that of the data analyst.

    Read also: Digital Nomad Jobs | Benefits, Disadvantages and List of Remote Works

    Which has a higher salary?

    As we mentioned earlier in this data analyst vs. data scientist comparison, more complex work usually means higher pay. Data science is no exception. However, how much better are data scientists paid than data analysts?

    According to Glassdoor.com, the average annual salary for a data analyst is around US$67 400. It would cost US$ 5 620 per month. That’s not a bad salary! However, the average annual salary for a data scientist is estimated to be around US$ 117 400, or nearly US$ 9 800 per month!

    That’s a huge difference! If we consider the difference between the two complexities of the job, it makes a lot of sense.

    Examples of Data Analyst assignments and missions

    Their main missions as a Data Analyst are generally:

    a. The creation of dashboards for monitoring KPIs

    This allows teams to more easily manage their activity without wasting time updating data. For example, a Data Analyst can create an automated dashboard for monitoring the performance and budgets of all the company’s marketing channels and thus calculate an aggregated acquisition cost.

    b. Carrying out A/B tests

    They can propose experiments (product, marketing, etc.), set them up with the operational teams and measure their impact on one or more given indicators.

    c. Carrying out ad-hoc analyses

    The Data Analyst can also explain certain variations in the KPIs by cross-referencing the data. They conduct the investigation to make recommendations.

    For example, they carry out customer segmentation analyses: by crossing all the characteristics of the users, it draws up a robot portrait of the most active users, or “dormant” users… To allow you to personalize your commercial or Product actions!

    d. Data management

    They bring knowledge of all the company’s data and he must manage his organization with the technical teams.

    Examples of Data Science assignments and missions

    Its main missions are generally:

    a. The creation of machine learning algorithms

    That is, algorithms aimed at predicting a phenomenon based on data from the past. Learning to detect users who will unsubscribe or even developing a recommendation system are classic examples.

    b. Analysis of the quality of the developed algorithms

    We must study the predictions that are made and ensure that the algorithm is still as efficient over time. Does the algorithm still manage to detect users who leave?

    c. The production starting bloc

    All the algorithms must communicate with the rest of the technical stack and must therefore obey quality and availability standards.

    What synergy between these two professions?

    The Data Analysts team helps Data Scientists identify the most relevant data to use for their algorithms. Data Analysts can also measure the impact of their algorithms in production on all company KPIs.

    The opposite of Data Scientists help Data Analysts in their analyzes by providing them with a whole set of new techniques.

    Together, they contribute to the evangelization of Data in the company by co-constructing what is called Data governance which designates both:

    • Internal training of other teams in Data
    • The creation of a “directory” of the metrics used and their calculations in their companies
    • Documentation on available data and its accessibility: who has access to what data in the company?
    • GDPR (General Data Protection Regulation) regulations

    It is hand in hand that they develop the company’s Data culture and define its organization.

    Sources: PinterPandai, ProjectPro, Towards Data Science

    Photo credit: Max Pixel (CC0 Public Domain)