With 17.8 billion connected devices worldwide, data is generated in enormous amounts from a wide range of sources in today’s data-driven world. Businesses have access to priceless chances to obtain insights that can spur growth thanks to this massive data flow. Data science and analytics are used by companies to gain valuable insights from this type of data. Although there are some places where these fields overlap, their fundamental roles and procedures are different.
This blog will discuss the important functions of data science and data analytics, their distinctions, and whether one can be the best option for your career.
Table of Content
What is Data Science?
Data science is a multidisciplinary field that use a variety of techniques, algorithms, and systems to extract valuable insights from both organized and unstructured data. Statistical approaches, programming, and machine learning are used to solve complex challenges and produce data-driven predictions.
In order to forecast future trends, data scientists frequently work with enormous volumes of data, extracting and cleaning it before using machine learning models.
Examples of data science products include Google Maps, which calculates the time it will take you to reach your location, and Netflix, which suggests movies based on your viewing history.
1. Data Science Process
- Data Collection: A variety of sources, including databases, APIs, and web scraping, are used to gather data.
- Data Exploration: Data scientists use exploratory data analysis (EDA) to look for trends and abnormalities in the data.
- Model Development: To provide predictions, machine learning models are created.
- Model Deployment: The models are used to make real-time, data-driven choices.
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What is Data Analytics?
Data analytics is concerned with analyzing data in order to find actionable insights that will assist businesses in making educated decisions. Analyzing historical data and producing reports that inform operational and strategic choices are usually its main objectives.
To find trends and patterns in data, data analytics uses a variety of statistical tools, frequently in conjunction with data visualization programs like Tableau and Power BI. For instance, a business may use sales information and client reviews to enhance its goods and services.
1. Data Analytics Process
- Data collection: Gathering necessary data from a range of sources, including files, databases, and surveys.
- Data cleaning: It is the process of preparing data for analysis by cleaning and organizing it.
- In exploratory data analysis, analysts search the data for important insights, trends, and correlations.
- Data visualization: This is the process of displaying the results in a graphical manner, like reports and dashboards, to assist stakeholders in making defensible choices.
Differences Between Data Science and Data Analytics
While both fields involve working with data, their objectives, methods, and outcomes are quite different. Below is a comparison of data science and data analytics.
| Aspect | Data Analytics | Data Science |
| Focus | Primarily, focuses on analyzing historical data to identify trends and make informed business decisions | Focuses on extracting knowledge and insights from complex and unstructured data to solve complex problems |
| Goal | To provide insights for making operational decisions and improving business processes | To derive actionable insights, build predictive models, and create data-driven strategies |
| Methods | Use descriptive and diagnostic analytics techniques to answer “what happened” and “why it happened” questions | Employs a broader range of techniques, including predictive modeling, machine learning, and advanced statistical methods to answer complex questions and make predictions |
| Skills | Strong proficiency in SQL, Excel, data visualization tools, and basic statistical analysis | Requires a deep understanding of programming languages (Python, R), statistics, machine learning, and domain knowledge |
| Tools | Excel, Tableau, Power BI, Google Analytics, etc. | Python, R, Hadoop, Apache Spark, TensorFlow, scikit-learn, etc. |
| Typical Role | Data Analyst, Business Analyst, and Business Intelligence Analyst | Data Scientist, Machine Learning Engineer, Statistician, and Data Engineer |
| Use Cases | Market research, A/B testing, customer segmentation, reporting, and dashboard creation | Predictive modeling, recommendation systems, natural language processing, computer vision, deep learning, etc. |
| Data Scope | Generally, deals with structured data and predefined metrics | Handles both structured and unstructured data, often dealing with big data and real-time streams |
| Decision-Making | Helps in making informed decisions based on historical data patterns | Provides insights, predictions, and prescriptive recommendations for strategic decision-making |
| Time Frame | Typically, focuses on short to medium-term insights | Involve long-term strategic planning and forecasting |
| Scale | Often operates at a smaller scale and may not require big data technologies | Often involves big data technologies and distributed computing for large-scale processing |
| Certainty | Generally deal with relatively certain outcomes based on historical data | Deals with a degree of uncertainty, especially in predictive modeling and future projections |
Here’s an example to help you understand the difference better:
- Data Science: Predicting what a consumer is likely to purchase next by analyzing their browsing history and past purchases.
- Data Analytics: Analyzing past sales data to discover which products are commonly bought together, like peanut butter and jelly.
Core Skills: Data Science vs. Data Analytics
We have gone through multiple job descriptions for Data Scientists and Data Analysts on LinkedIn. Below is the basic skill requirement schema followed by major organizations:
1. Data Scientist Skills
Source: TCS Job Description LinkedIn
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2. Data Analyst Skills
Source: Amazon Job Description LinkedIn
Tools Used in Data Science and Data Analytics
| Categories | Data Science | Data Analytics |
| Programming Languages | Python, R, and Scala | SQL, Python, and R |
| Data Manipulation | Pandas and NumPy | SQL and Excel |
| Statistical Analysis | SciPy and Statsmodels | Excel and SPSS |
| Machine Learning | Scikit-learn, TensorFlow, and PyTorch | Scikit-learn and RapidMiner |
| Data Visualization | Matplotlib, Seaborn, and Plotly | Tableau, Power BI, and QlikView |
| Big Data Processing | Apache Spark, and Hadoop | Apache Spark, Hive, and Pig |
| Version Control | Git and GitHub | Git and GitHub |
| Data Warehousing | Amazon Redshift and Google BigQuery | Snowflake and Amazon Redshift |
| Cloud Computing | Amazon Web Services (AWS) and Microsoft Azure | AWS and Google Cloud Platform (GCP) |
Processes in Data Science and Data Analytics
Both fields involve specific processes tailored to their respective goals:
1. Data Science Process
1.1. Web Scraping and Data Collection
There are a range of sources from which the experts get their data. As this is the first step in the Data Science process, it’s all done using Database access, web scraping, and APIs.
1.2. Data Management
After gathering the Data, experts focus on managing and organizing the data (Data Management). This includes activities such as data integration, data transformation, data cleaning, and handling missing values.
1.3. Exploratory Data Analysis (EDA)
EDA is that tool which Data Scientists utilize to study data characteristics and find patterns, anomalies, and correlations. This step uses data visualization software and other significant statistical techniques to get insights.
1.4. Model Development
The data scientists design either a descriptive or predictive model based on machine learning algorithms and statistical techniques. It involves the evaluation, training, and selection of the correct model and improvement of features to make better predictions.
1.5. Model Deployment
The data scientists design either a descriptive or predictive model based on machine learning algorithms and statistical techniques. It involves the evaluation, training, and selection of the correct model and improvement of features to make better predictions.
2. Data Analytics Process
2.1. Data Collection
The very first step of the data analytics process is Data Collection from various sources. It may include data extraction from flat files, databases, or APIs. More methods for gathering data include surveys and manual data entry.
2.2. Data Cleaning
It is basically preparing the data for analysis and cleaning. The job is mainly carried out by data analysts. This usually deals with missing values, removal of duplicates, standardization of formats, and fixing inconsistencies in data.
2.3. Exploratory Data Analysis and KPI Configuration
In an exploratory data analysis process, data analysts look for patterns, trends, and even correlations in the data but also observe the nature or characteristics of the data set. They also establish certain performance indicators, known as key performance indicators (KPIs), to track a specific metric relevant to an investigation’s goals.
2.4. Analyzing Data with Power BI and Tableau
Power BI and Tableau are technologies used by data analysts to carry out comprehensive data analysis, creating visualizations and reports and dashboards that can help them create insightful reports. Such dynamic and interactive visualizations can assist in making decisions based on data.
2.5. Creating Presentations for Communicating Findings to Stakeholders
Data analysts create reports and presentations to effectively communicate their understandings and results to the management teams, customers, and other stakeholders. At this point, the key findings are outlined, illustrations are provided, and useful recommendations grounded on the analysis are made.
Which One is Right For You?
Choosing between data science and data analytics depends on your interests and career goals. If you enjoy analyzing past data and generating insights for immediate business decisions, data analytics may be a better fit. If you’re passionate about predictive modeling, machine learning, and working with large datasets, data science might be the right choice.
Salary Comparison: Data Science vs. Data Analytics
1. Data Scientist Salary in India
In India, the range lies between ₹8 lakh to ₹20 lakh, with an average salary of ₹12,60,325 (according to Glassdoor).
2. Data Analyst Salary in India
In India, the range lies between ₹4 lakh to ₹10 lakh, with an average salary of ₹6,60,750 (according to Glassdoor).
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Future Prospects
Both data science and data analytics are booming fields with significant job opportunities.
Data Scientist Jobs in India: Over 122,000 job openings (according to LinkedIn).
Data Analyst Jobs in India: Over 132,000 job openings (according to LinkedIn).
Both fields offer strong career growth, with data scientists often having a higher earning potential due to the specialized skills required.
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Conclusion
In today’s data-driven environment, data analytics and data science have different but equally important responsibilities to play. While data analytics is focused on examining past data to enhance decision-making, data science is more concerned with resolving difficult issues and forecasting future trends.
Your long-term objectives, interests, and skill set will all influence your job choice. Whichever route you go on, there are plenty of chances for advancement, influence, and good pay in both industries. Check out our Data Science and Data Analytics Course if you want to learn more and pursue a career in either of these areas.
Frequently Asked Questions (FAQs)
Who earns more data scientist or data analyst?
In India, the average compensation for a data scientist is INR 10.5 LPA, compared to INR 6 LPA for data analysts. Both job descriptions have plenty of options for career development.
Can a data analyst become a data scientist?
Working as a data analyst initially might be a great way to get started in the field of data science.
Does a data analyst require coding?
Yes, if you want to pursue an online degree in data analytics, you must know how to code. It does not need extremely sophisticated programming knowledge. However, learning the fundamentals of R and Python is required. Additionally, having considerable knowledge of querying languages like SQL is more than required.
What should I learn first, data science or data analytics?
A beginner without programming experience could find that starting with data analytics tools like Microsoft Excel and Power BI is an appropriate choice.
Is data analytics harder than data science?
Data science is more difficult because it requires a greater knowledge of statistics, mathematics, and machine learning. Data analysis is needed for the same degree of statistical knowledge as well as familiarity with using software programs like Tableau or Excel.