Information technologies constantly develop and allow organizations to record nearly every step of their stakeholders in raw data. That large pool of data needs to be further analyzed to make decisions; here, data scientists play their primary role.
Data scientists work with structured and unstructured data to extract insights that otherwise can be unnoticed by business stakeholders. To figure out what data has to tell the company, data scientists perform exploratory analysis, identify patterns, make predictions, and advise decision-makers based on the analysis results.
Without data science, companies do not have a solid base for designing strategies. That’s why data science grows at intense rates, and according to Turing Award winner Jim Gray, is considered as the fourth paradigm of science after empirical, theoretical, and computational.
At this point, you might question whether it’s too hard to become a data scientist and what programming language you should learn to become one.
This article exactly answers your question. Read on.
What is data science?
Data science is the art of extracting, analyzing, visualizing, managing, and storing data. It is a multidisciplinary field that combines statistics, math, and computer science to help organizations take actionable insights from data and apply them to their decisions.
To be more specific, here are the two main types of data analysis data scientists perform.
Predictive
Predictive causal analytics benefit those organizations which largely depend on the assessment of future risks. Predictive analytics forecasts future events based on previous occasions.
For example, an organization that provides loans should conduct careful research on each client to understand whether it’s worth lending him money. Having recorded data of the client’s past payment history, data science can predict the client’s probability of returning the money on time.
Prescriptive
Prescriptive analytics does the same as predictive analytics and also provides suggestions on prescribed actions and associated outcomes.
For example, if you want to create a self-driving car, you can collect the data on many vehicles, then run algorithms to that data and receive an artificial intelligence.
The latter will make a new self-driving car decide how it should behave on the road based on the previous performance of similar cars. For example, a vehicle can choose when to turn, stop, or speed up on the road.
What do data scientists do?
A typical routine of data scientists’ work comprises the following five stages: the data science life cycle.
Capture
Capture or Data Collection is the step every data scientist starts their project with. Once there is a problem that needs to be solved, the task of the data scientist is to think of where and how he can find the answers.
For this reason, data scientists collect raw data from all the available sources. If the relevant data for the specific issue is not collected, data scientists can organize the collection processes themselves.
There is no specific way of how specialists can capture the necessary data. The methods may vary from the most straightforward web scraping and manual entry to querying databases with tools like MySQL.
It’s widely accepted to collect all the data in Tab Separated Value (TSV) or Comma Separated Value (CSV) formats. Also, you might usually need specific packages of Python or R to read the data.
Prepare and maintain
The data collected in its raw form is not applicable for the analysis. Data science programs might not read the data and perform analysis if the data is not converted into an appropriate format and is not cleaned.
Cleaning the data usually means withdrawing or replacing N/A values, removing missing data sets, deduplicating, etc.
The stage of preparing the data for further analysis is not a mechanical task. It includes removing the raws or columns that might further cause an error in the program and reworking on inconsistencies and non-logical patterns.
For example, your data consists of the survey results where the participants are digital marketers. If you see, the majority of the survey participants answered “Very low” to the question of “What is the level of your understanding of SMM?”, you might conclude there was a certain technical issue, since the majority of digital marketers can’t have very bad understanding of SMM.
Consequently, the better you know the industry or the field you work for, the easier it is to clean your data.
Explore
This stage is also called preprocess or process, where data scientists examine or inspect the data. For this step, specialists compute descriptive statistics, which gives a quick summary of the data values.
This means you can explore the data types; nominal and ordinal, numerical and categorical. That’s essential as each of the mentioned data types needs a different approach during the analysis.
Further, data scientists use data visualization to get a deeper insight into trends and patterns of the data. Based on the above example, you can see the age of the surveyed digital marketers, their monthly wages, conducted projects during the month, and many more.
This helps data scientists understand which variables they have at hand and how they can use them to extract insights from the data.
Analyze
This is the main stage where data scientists deeply analyze the data and make conclusions. Data scientists might use different programming languages for the analysis; however, Python is the absolute leader in this regard. Therefore, if you want to become a data scientist, you should consider learning Python as one of your first to-do steps.
Below are the main processes that usually happen during the analysis stage:
- Linear regression for making predictions.
- Logistic regression to differentiate and classify values.
- Decision trees to predict a variable based on the observation attributes.
- Cluster analysis to group related data variables.
- Machine learning to identifying patterns in the data.
If you want to understand each of those processes in more detail and master the essential functionality of Python for data science, head to our 34 lessons of the new course of Python.
Communicate
As mentioned at the beginning of this article, data scientists identify and communicate insights from the raw data that might otherwise remain unnoticed by organizations and businesses.
Therefore, one of the critical soft skills that data scientists should have is communication. The final results of the data analysis should be organized in an easily scannable way, as often the key stakeholders of the research project might not be familiar with Python or its interface.
Usually, data scientists organize their findings in reports, where the main patterns are visualized. You can include many charts and graphs and use easy wording to communicate your suggestions to the organization.
To quickly generate the reports, you can use the Matplotlib visualization library from Python and create a visual representation of data distribution based on the variables you choose.
Head to our newly introduced course, Python for data science, for more.
