Imagine getting into Data Science and realizing it’s not your career-match. Or, becoming a Data Analyst and eyeing the package of a Data Scientist. Data Analytics and Data Science are the big talks of 2023. If you’re torn choosing between these fields, Data Analytics vs Data Science for your career or for simply understanding what’s the difference, this article is for you.
People often confuse Data Analytics and Data Science as the same thing but they are not.
It’s true that both deal with data, but the approach has a hell & heaven difference. However, there is a common ground too, and Data Analytics can be seen a stepping stone to becoming Data Scientist in the longer run.
In this article we will help you understand:
- The differences between Data Analytics vs Data Science in everyday language along with examples
- Career scope in Data Analytics vs Data Science
- Latest upgrades in Data Analytics this year vs Data Science upgrades
- Skills needed to be a Data Analyst or Data Scientist
- Topics that you must learn
- Finally, how to start a career in these fields, or what is the education level required to enter these fields.
What is Data Analytics?
In simplest terms, there are two components in the name “Data Analytics”: Data and Analyzing it (with Mathematical tools such as Statistics). Data Analytics also requires a basic knowledge of coding.
The job of a Data Analyst is to come up with insights that can help a business become better (& make more money by being better).
So, a Data Analyst examines data and draws conclusions about the information they contain. Technically speaking, it’s the process of cleaning, transforming, and modeling data in order to find patterns and insights that can help make better business decisions.
What is Data Science?
Data science is a rapidly growing field with 27.6% CAGR. It requires more technical expertise compared to Data Analytics. And if you are someone who dreads coding, then it may not be for you.
Data Science uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. For this, you need to be adept with analytical, statistical, and programming skills to uncover patterns and trends in data that can be used to solve REAL-WORLD problems.
Categories (with Real-Life Examples):
Categories of Data Analytics:
1. Descriptive analytics:
- Using data to understand what has happened in the past.
- It uses tools like charts, graphs, and dashboards to represent data visually, so that patterns and trends can be easily identified.
Real Life Example:
If you work at a retail store and you want to know how many items you sold last year, you would use descriptive analytics to gather that information. You might create a bar graph or pie chart to visually represent the data.
2. Predictive analytics:
Predicting the future may seem like an insane idea until Predictive analytics steps in.
- Using statistical and machine learning algorithms we can identify patterns in data that can be used to predict future outcomes.
Real Life Example:
If you work for an insurance company, you might use predictive analytics to determine the likelihood that a customer will make a claim. You would use historical data to build a predictive model that can predict the likelihood of future claims.
3. Prescriptive analytics:
- Like a doctor handing out prescriptions to you, recommending medicines for your illness, data can help prescribe measures that’ll work out the best in a situation.
- It uses advanced algorithms to determine the best course of action based on data analysis.
Real Life Example:
If you are a hospital administrator, you might use prescriptive analytics to determine the best course of treatment for a patient based on their medical history and current symptoms. You would use algorithms to analyze the data and determine the most effective treatment plan.
In short, this is how the life of a Data Analyst looks like:
- Typically work with data that is already structured and cleaned.
- Use tools like SQL and Excel to extract, clean, and analyze data.
- Use visualization tools like Tableau and Power BI & create dashboards and reports that help the team to understand the insights clearly.
Categories of Data Science:
- Often data sets can reveal surprising insights and patterns. Identifying these patterns and relationships in data, using visualizations, to get a sense of what the data is saying is called Exploration.
- Predictive modeling involves using machine learning algorithms to build models that can predict future outcomes based on historical data.
- For optimization, the algorithms are used to find the best solution to a problem, given a set of constraints.
Real-Life Examples & Applications:
Data science is being used in the real world to solve complex problems! Here are just a few examples:
1. Predicting Customer Behavior: Retailers and e-commerce companies use data science to predict customer behavior, such as what products they are likely to buy (& show them recommendations accordingly), when they are likely to buy them (& this can help to place the ads accordingly on the Instagram) , and how much they are willing to pay.
2. Disease Diagnosis: Healthcare providers use data science to analyze large sets of patient data to develop predictive models for disease diagnosis and treatment.
3. Fraud Detection: Financial institutions use data science to detect fraudulent activity in real-time, allowing them to prevent financial losses and protect their customers. Have you ever received an email about “suspicious activity” or “suspicious login” detected?
4. Traffic Optimization: This may seem like we are living in the future but cities use data science to optimize traffic flow by analyzing data from traffic sensors, GPS devices, and other sources.
5. Natural Language Processing: Companies use data science to develop natural language processing models that can understand and analyze human language, allowing them to automate customer service and other tasks.
I could quote a chatbot here or even Alexa or Siri. Has Alexa or Siri ever surprised you by understanding & responding to something you didn’t expect it would?
Skills needed for Data Analytics:
(Data Analytics & Data Science, both require technical and soft skills. So, only choose a career in data when you have it honed both ways)
1. Knowledge of data analysis tools like Excel, SQL, and visualization software
2. Understanding of statistical methods and techniques
3. Ability to communicate insights to stakeholders
4. Attention to detail and ability to work with large data sets
5. Ability to work with structured data
Skills Needed for Data Science:
Data Scientists require much high proficiency of Python, R, and SQL, compared to Data Analysts.
2. Data Manipulation:
An ability to extract, clean, and manipulate data is a must, using tools like Pandas, and NumPy.
3. Machine Learning:
Data scientists should be familiar with machine learning algorithms and techniques, such as regression, clustering, and decision trees.
4. Data Visualization:
A Data Scientist’s job doesn’t end at gaining the insights. These insights are usually extremely complex. That is why they must produce compelling visualizations that communicate insights to stakeholders using tools like Matplotlib, Seaborn, and ggplot2.
Soft Skills which are a MUST for both Data Analytics & Data Science:
1. Communication: Communicating complex ideas and insights to simple visuals so that non-technical stakeholders can easily understand.
2. Problem-Solving: With every Data based project there is a problem that one is trying to solve or a question that one is trying to answer. So, one should be able to identify and solve complex problems using analytical and critical thinking skills.
3. Curiosity: Natural curiosity is a must to explore new ideas and approaches, which leads to better results and a successful career in the longer run.
Topics to learn Data Analytics:
1. Data analysis tools and techniques
2. Statistics and probability
3. Data visualization
4. Data Reporting
5. Database management
Topics to learn Data Science:
1. Machine Learning & AI
6. Big Data
7. No SQL
8. Data Wrangling & Mining
10. Data Reporting
11. Data Science Tools & Techniques
Career scope in Data Analytics
Data analytics is a growing field, with job opportunities in industries such as finance, healthcare, marketing, and e-commerce. Some common job titles in this field include data analyst, business analyst, financial analyst, and marketing analyst. According to Glassdoor, the average salary for a data analyst in the United States is $62,000 to $92,000 per year.
Career Scope in Data Science
A LinkedIn report says that data science is the second most promising career of 2021, with a 26% year-over-year growth rate in job openings.
The demand for data scientists is increasing rapidly, as more and more companies recognize the value of data-driven decision making. According to the Bureau of Labor Statistics, employment of computer and information research scientists, which includes data scientists, is projected to grow 15 percent from 2019 to 2029, much faster than the average for all occupations.
On an average a Data Scientist can expect to earn around $120,000 per year, in the United States, according to Glassdoor.
Data scientists can work in a wide range of industries, including healthcare, finance, retail, e-commerce, government, and more. Some common job titles include:
1. Data Scientist
2. Machine Learning Engineer
3. Business Intelligence Analyst
4. Data Analyst
6. Data Engineer
Latest upgrades in Data Analytics 2023
The field of data analytics is constantly evolving, with new tools and techniques being developed all the time. Some of the latest upgrades in the field include:
1. Advanced machine learning algorithms that can handle unstructured data
2. Cloud-based data analytics platforms that can handle large amounts of data
3. Natural language processing tools that can analyze unstructured text data
4. Integration with data visualization tools to create more interactive dashboards and reports
Latest Upgrades in Data Science 2023
Data science is a rapidly evolving field, and new technologies and techniques are constantly emerging. Some of the latest upgrades in data science include:
1. AI is here to help! It helps Data Scientists to automate tasks and analyze large datasets more quickly and accurately.
2. Deep learning, a subset of machine learning, involves training neural networks to recognize patterns and make predictions based on large datasets.
3. NLP, or Natural Language Processing, is a branch of AI that focuses on enabling computers to understand and interpret human language.
If you begin your career in Data Analytics, it is possible for you to advance to Data Science with more learning and experience. Based on all the information provided in this post: Data Analytics vs Data Science, it must be clear that for proficient coders Data science will be easy to pick. But for a fresher in coding or coding-averse person, Data analytics will be more suitable career option.