What is data science in simple words?
Let’s understand what data science is in simple words with an example. Imagine you and your friends were debating on a single topic.
The topic: Does Social Media Usage Impact Mental Health?
Now you were saying it does and your friend said it is just a rumour. And in just a few minutes you were like this :
Now this is nothing new. We wake up with the news of Companies falling, CEOs breaking up and one of the big reasons is fights. Any Fights and disagreements can be solved with data analysis.
How can you solve this solution?
You and your friend decided to send a form to collect data of 10000 volunteers and came up with 10000 rows of data.
- Name: Participants’ names or unique identifiers.
- Age: Age of each participant.
- Hours of Social Media Use: Self-reported hours spent on social media per day.
- Mental Well-being Score: Self-reported mental well-being score, which could be a single rating (e.g., on a scale from 1 to 10) or based on a standardized assessment tool (e.g., well-being questionnaire).
Now many of the volunteers made many mistakes like suppose they entered unrealistic numbers or wrote hours and minutes. Now this part is difficult and you will have to clean and prepare data by going back to volunteers and using some statistical and cleaning methods.
Now you will be doing different kinds of analytics.
Descriptive analytics simply means what happened. We uncover the insights that are already in the data. We perform Exploratory Data analysis and find answers about the average age of participants or highest and lowest scores.
Descriptive Analytics Examples:
- Calculate average age.
- Identify the most used social media platform.
- Find the highest and lowest mental scores.
- Calculate average hours on social media.
- Analyze male and female usage.
- Determine the common age range.
- Understand social media platform distribution.
- Calculate the mental well-being score range.
- Find median social media usage.
- Analyze gender-based social media platform usage.
- Calculate the percentage exceeding social media use guidelines.
- Understand the frequency of social media engagement.
- Assess variability in mental well-being scores by age.
- Determine the proportion of reporting social media-related stress.
- Compare social media usage by education level.
Diagnostic analytics tells you why something happens. When we find something fishy is happening in descriptive analytics and want to know the reason behind it. We use Diagnostics analytics like :
- See if older people use social media differently.
- Check if certain social media choices affect how people feel.
- Look at how much social media use relates to feeling good or bad.
- Check if talking more on social media links to feeling better.
- See if men and women feel differently based on social media use.
- Look at whether education affects how people use social media.
- Compare how happy people are on different social media sites.
- See if using social media at certain times changes how people feel.
- Check if spending a lot of time on social media makes people more stressed.
- Explore if seeing negative things on social media makes people feel worse.
Predictive analytics is to find pattern and trends in the dataset and estimating the future. Some of the examples are :
- Guess how people will feel later based on how they use social media now.
- Predict which social media platforms will be most popular next year.
- Estimate how much time people will spend on social media in the future.
- Guess if using social media less will make people feel better in the future.
- Predict if older or younger people will use social media differently in the future.
- Estimate how often people will like or comment on social media in the future.
- Guess how much time different age groups will spend on social media later.
- Predict if people will feel happier or sadder on social media soon.
- Estimate how social media use might change with new technology.
- Guess if people will become addicted to social media based on their current habits.
Prescriptive analytics is the most important analytics and we ultimately solve the whole situation and come up with the prescription and conclusions.
You do not need to be intelligent to do these things. All you need is skillsets. Companies collect your data all the time and they are in serious need of data analysts.
Anyone can become a data analyst. All you need is the right skillset and great mentors. We at Consoleflare has successfully placed thousands of students in Fortune 500 companies.
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