The 4 Important Aspects of Data Science

Data science is the backbone of informed decision-making in companies. It is a discipline that gathers, analyzes, and makes sense of large data sets. Data science is a large field that encompasses a wide range of tasks. Those on a data science career path need to be versed in many areas. Let’s dig deeper into the most important aspects of data science. We’ll tell you what you need to know and give you information on the types of companies and areas looking to hire data scientists.

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1. Data Collection

Data collection involves gathering data for business decision-making, strategic planning, research, and other purposes. Data collection can be conducted manually (think surveys and focus groups) or automatically (think sensor-based tracking). Data collection methods are divided into three main categories: quantitative, qualitative, and a combination of both. 

Quantitative data is numerical information collected via surveys, polls, and experiments. Qualitative data, on the other hand, is non-numerical and is collected via in-depth interviews, focus groups, and observations. Information gathered through a combination of both quantitative and qualitative methods is known as “mixed” methods.  

Structured vs. Unstructured Data

Data collection methods are further divided into structured and unstructured methods, as well as active and passive methods. 

Structured data collection methods have a set order and pattern and are typically quantitative. Structured data collection methods are often quicker and easier to implement than unstructured data collection methods. However, they often lack the flexibility of unstructured data collection methods. 

Structured data collection methods are often the best option for large-scale studies. Data collection methods are not mutually exclusive; in many situations, a combination of structured and unstructured data collection methods is the best approach.

Unstructured data does not have a set order or pattern. Examples of unstructured data include blog posts, comments on social media sites, emails, feedback forms, surveys, and other types of communication. Companies often use artificial intelligence (AI) and natural language processing (NLP) software to extract insights and information from this information. 

Active Data vs. Passive Data

Active data collection requires someone to seek out the data required for their research. This method is often preferred over passive methods because it allows the researcher to be more intentional with the data being collected and has the added benefit of helping researchers avoid common sampling biases inherent in passive data collection methods. Data collection methods include surveys, experiments, focus groups, and observations.  

Passive data collection methods are automated. Examples of passive data collection methods include using server log analysis to track website traffic, using Google Analytics to track the demographics of website visitors, or installing software on company computers to track employee productivity. Passive methods are often the easiest way to get data. However, they may not produce the most accurate results. 

2. Data Cleaning and Transformation

Many people view data cleaning as a less glamorous aspect of data analytics. But it is an essential part of the process. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled. 

If data is incorrect, outcomes and algorithms are unreliable. Cleaning the information involves fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, and incomplete data within a dataset. There are many different types of errors that can occur in datasets:

• Missing Values: The most common type of error is a missing value (also known as an “NA” or “null”). A missing value occurs when an entry in a dataset does not have a value assigned to it. 
• Duplicates: Duplicate records occur when two or more records have the same values for all variables. Duplicate records cause problems with statistical analysis because they skew results and make it difficult to draw conclusions. 
• Outliers: Outliers are extreme observations that deviate from the rest of the dataset. Outliers are either much larger or smaller than the rest of the observations. 

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Data Transformation

Data transformation changes data from one format into a new format that’s more useful for analysis. Data transformation is often the first step in a data pipeline, and it is essential to ensure that the data is useful. Companies use Extract, Transform, and Load (ETL) tools to transform the information. The most common data transformation tasks are: 

• Converting data from one format to another (e.g., from CSV to JSON)
• Normalizing data (e.g., removing white space, fixing spelling mistakes)
• Enriching data with additional metadata (e.g., adding timestamps); 
• Removing sensitive data (e.g., Social Security numbers)

Scientists use ETL tools to automate extracting data from its source, moving it to a staging area, and then loading it into the final data warehouse or data lake. 

3. Statistical Analysis

The ubiquity of data in the digital age makes statistical analysis an essential business skill. Data is generated every time someone makes a purchase, completes a survey, or sends an email. Statistical analysis involves collecting, exploring, and presenting large amounts of data to discover underlying patterns and trends. 

Statistical analysis allows you to compare results to past performance, benchmark against industry averages, measure progress against goals, and identify any outliers. It’s a numbers-based approach to solving problems, testing hypotheses, and making decisions. There are several types of statistical analysis, but the most common are exploratory and confirmatory. 

Confirmatory Analysis

Confirmatory analysis refers to testing a hypothesis against a data set. Scientists use it to test a hypothesis and explore the possibility of a relationship between two or more variables. Confirmatory analysis is particularly useful for exploratory analysis because it enables you to create a dataset for future hypothesis tests. 

Confirmatory analysis is most often used in the social sciences and applied sciences where hypotheses are difficult to test due to complexities or ethical considerations. This type of analysis often involves a smaller sample size than exploratory analysis. Why? Because it is trying to prove something rather than explore a hypothesis. This type of analysis involves testing different variables to see which produces the most beneficial results. 

This approach requires much more rigor than exploratory analysis. It is also more costly and time-consuming to conduct. Confirmatory analysis requires a large control group or a larger sample size. 

Due to the increased rigor required, confirmatory analysis often requires more precise and quantifiable questions than exploratory analysis. 

For example, instead of asking “what is the best product to sell online?” you would ask “what is the best product to sell online that also has a profit margin of at least 20%?” This more precise question will lead to a more accurate analysis.

Confirmatory analysis is often used to validate analytical findings from exploratory analysis. An example of this might be determining which variables in a given model are statistically significant. Confirmatory analysis is often much more precise and quantifiable than exploratory analysis, but it is less exploratory than inductive analysis. Confirmatory analysis often relies on exploratory analysis as a foundation.

Exploratory Analysis

Scientists use this approach to discover patterns and trends in data with no hypothesis in mind. This type of analysis is exploratory. It is not driven by any expected results, and the results may not be actionable. The purpose is to observe the correlations between different data points to identify patterns. 

4. Data Visualization

Data visualization is the process of creating interactive visuals to quickly understand trends and variations and derive meaningful insights from the information. Data visualizations are represented as graphs, charts, tables, maps, etc. 

They are the best way to share information with the team, stakeholders, and customers. Visuals make the data easier to digest and they are easier to share. the most common types of visualizations are:

• Graphs
• Charts  
• Tables
• Maps

Data visualizations benefit organizations in many ways. They are useful for allowing businesses to take quick action in their operations. They also provide a detailed analysis of the data for the comparison and identification of patterns. They simplify and make data easier to understand and consume for non-technical users.

Data visualizations are helpful in communication, both internally and externally. They are a quick and easy way to share information and data with stakeholders, partners, customers, and employees. Additional benefits include:

• Identify Patterns in Operational  Data: Data visualization techniques help scientists understand the patterns of business operations. By identifying solutions in terms of patterns, data scientists apply these lessons to eliminate one or more of the inherent problems.

• Identify Market Trends: These techniques help us identify trends in the market by collecting data on daily business activities and preparing reports. This helps track the business and reflect on what influences the market. These reports are beneficial for the organization as they help in taking quick actions to adjust to the ever-changing market conditions.

• Identify Business Risks: These techniques help us identify risks by collecting data on daily business activities and preparing reports. This helps track the business and reflect on what influences the risk factors in operations. These reports are beneficial for the organization as they help in taking quick actions to avoid adverse consequences from those risks.

• Storytelling and Decision-Making: Knowledge of storytelling from available data is one of the niche skills for business communication, specifically for data science. It helps to know how to frame the data in a way that is most meaningful to the audience. This storytelling is accomplished when data scientists know how to find the story within the data. The best data scientists know how to construct a narrative from data by asking the right questions. They know how to find cause and effect within the data as well as how to find a common thread. They know how to frame the data in a way that is most meaningful to the audience.