Data science aims to derive significance and understanding from data analysis and statistical methods. A data scientist may explore the correlation between variables or assess public sentiment via surveys.
Familiarizing yourself with a data scientist’s tasks and the underlying concepts can assist in determining if this field aligns with your interests. In this piece, we explore the essence of data science and enumerate key concepts commonly employed by data scientists.
Data Science
Data science involves extracting meaningful insights from data. A data scientist may analyze data to identify hidden trends or patterns. At times, algorithms or other techniques are employed to process data for valuable information. The primary objective of data science is to unveil insights and make informed decisions based on those findings.
Data science encompasses two main categories: descriptive statistics and inferential statistics.
Descriptive statistics interprets data and draws observations directly from the data. On the other hand, inferential statistics extrapolates broader conclusions from a subset of data points. For instance, a political survey represents an instance of inferential statistics in action.
Here are 13 statistical concepts that are valuable for data scientists:
1. Data Types and Measurement Scales
Data comes in different types, and understanding these types is essential for selecting appropriate statistical tools and methods.
Quantitative and Qualitative Data
Quantitative Data: This type involves numerical values that can be measured and used in mathematical calculations. It answers questions like “how much” or “how many,” such as the number of website visitors or the temperature of a city.
Qualitative Data: In contrast, qualitative data describes characteristics or attributes. It deals with “what type” or “which category,” such as the color of a car or the genre of a book. This type of data is subjective and based on observations rather than numerical values.
2. Four Scales of Measurement
Nominal Scale: This scale categorizes data without a specific order, such as types of cuisine or blood groups. It involves labeling without numerical value.
Ordinal Scale: Data in this scale can be ranked or ordered, but the intervals between values are not defined. For example, a satisfaction survey with options like satisfied, neutral, and unsatisfied.
Interval Scale: Here, data is ordered, and the difference between entries is quantified. However, there is no true zero point. An example is temperature in Celsius.
Ratio Scale: The most informative scale, it includes all properties of an interval scale plus a meaningful zero point. Examples are weight, height, and income, allowing for accurate comparisons of magnitudes.
3. Define a problem
Defining a problem provides clarity on what a data scientist aims to achieve through statistical analysis. This understanding is pivotal in predictive modeling as it directs the selection of methodologies and relevant concepts.
By defining the problem, data scientists determine the necessary data inputs and applicable metrics aligned with the objectives.
For example, when conducting a political poll in a specific congressional district, understanding the district’s party demographics—such as the number of registered Democrats and Republicans—ensures that the sampling accurately represents the area.
4. Sampling Techniques
Sampling techniques involve gathering data from a representative sample to draw broader conclusions.
For instance, in political polling, a pollster might randomly call 1,000 individuals. However, this sample may not accurately represent the entire voter population, potentially under-representing certain demographics or groups.
To address this, pollsters can adjust the data by weighting responses, ensuring the sample reflects the overall population.
5. Testing Hypotheses
Hypothesis testing in data science involves making an inference from data and then validating that inference with new data.
This process may include re-sampling techniques and comparing outcomes to determine statistical significance.
For example, an A/B test evaluates two different variables, such as subject lines in marketing emails, by comparing metrics like open and click rates to identify the more effective option.
6. Variation
Variation refers to the natural fluctuations and shifts observed in data points. For instance, if the average
Friday night movie theater attendance is 100 people, it doesn’t mean exactly 100 people attend every Friday; there will be inherent variances.
This variation can include outliers, such as a night with 200 attendees, which may be excluded from analysis to better understand typical attendance patterns.
7. Bayesian Statistics
Bayesian statistics utilizes data to forecast the likelihood of future events, considering additional factors that the data scientist anticipates will be valid in the future.
For instance, if one predicts that an annual rock concert typically draws 100,000 attendees over three days based on past events, Bayesian modeling factors in potential changes like more musical acts and a larger, more accessible venue, influencing the predicted attendance.
8. Covariance
Covariance measures the variability between random variables, indicating how they deviate from their means.
For example, if two random variables consistently deviate in a similar manner, there may be covariance between them. Data scientists investigate whether this covariance is caused by a common factor or if the data points share a common characteristic.
9. Regression Analysis
Regression analysis delves into the connection between two variables. For instance, within a business context, augmenting staff often correlates with increased costs and productivity.
Through regression analysis, one aims to pinpoint the juncture where the cost hike due to additional staff nullifies the productivity gains.
Data scientists rely on regression to identify where investments might lead to diminishing returns.
10. Variability
Covariance gauges the fluctuation between random variables, showcasing their deviation from averages.
For instance, if two random variables consistently exhibit a similar deviation pattern, covariance may exist between them.
Data scientists investigate whether this covariance stems from a shared factor or if the data points possess a common attribute.
11. Regression Analysis
Regression analysis examines the relationship between two variables. For instance, in a business context, increasing staffing typically leads to higher costs and productivity.
A regression analysis aims to determine the point where the cost increase from additional staff offsets the productivity gains.
Data scientists use regression to pinpoint where investments may yield diminishing returns.
12. Probability Distribution
This concept assesses the likelihood of an event occurring. For instance, in sports, probability distribution calculates the probability of a team making the playoffs based on their current performance.
By analyzing past playoff teams’ performance and current records of other teams, a data scientist can estimate the percentage chance of a team making it to the playoffs.
For example, a team starting their season with a 3-0 record might be given a 75 percent chance of making the playoffs based on historical data and performance trends.
13. Dimension Reduction
Dimension reduction involves managing the quantity of random variables, aiming to decrease them by defining parameters and data features. This process enables data scientists to concentrate on specific data for analysis, thus enhancing the efficiency of algorithm development.
