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A Thorough Guide To Data Science And Data Science Careers

Whether you are looking to further your skills and deepen your knowledge on everything related to data science for the pursuit of a professional career in the discipline, or you are merely fascinated by the minutiae and technical details of data science, there is no doubt that you have come to the right place. 

With that in mind, continue reading this article for a thorough and comprehensive guide to data science and a detailed overview of possible careers in the field. 

Data Science: Explained

Essentially, the field of data science combines programming skills, domain expertise, and a knowledge of statistics in order to extract meaningful insights from data.

To immerse yourself entirely in the incredibly complex and challenging (but equally rewarding and beneficial) world of data science, it is strongly advisable to that you embark upon an online master of data science postgraduate degree from a reputable, renowned, prestigious, and established online academic institution.

The Lifecycle Of Data Science

In order to understand the fundamentals of data science properly and succinctly, there is an industry-recognized model to help identify the key components of the discipline—also known as the lifecycle of data science:

Section 1: Business Understanding

Business and data understanding are crucial to the proper application of data science to a specific company, and unfortunately, this is a step that many data scientists (or rather, other professionals who are engaging in data science) neglect.

There are numerous pitfalls for a data scientist who doesn’t first ascertain the needs of the business and strive to gain a thorough understanding of the entire business model and the inner workings of the company. 

Such pitfalls include gaining wholly inaccurate insights that provide no significant impact or value to the business in question—even when the findings enter the production stage and the data scientist themselves is working alone without the backing, understanding, and collaboration of other members of management and business executives. 

Section 2: Data Mining

The second stage of the lifecycle of the successful application of data science to a business model or group of companies is to engage in data mining. 

Data mining is essentially the field in which computer scientists analyze large databases—often from numerous different sources—to succinctly, effectively, and accurately generate new data and information that can aid the business model, and therefore grow and expand the company. 

Within the discipline of data mining, there are three fundamental and wholly necessary techniques, which will produce the most successful (and crucially, the most useable) results:


Association is one of the most important and popular ways of gaining new insights through data analysis and data mining, and it essentially involves discovering and ascertaining a seemingly unrelated link between two different events or activities within a business model. 


Data science professionals who regularly use data mining to collect and collate information and data from a range of sources often use the tool of regression, which is more of a mathematical tool. Regression analysis is used to estimate a number using established patterns in the company’s history in order to produce projected results in the future. 


The third (but just as established and utilized) method used in the data mining process is referred to as clustering. Clustering is essentially an approach which shuns estimations and future data predictions and instead focuses its attention on grouping types of data by their similarities to each other. 

Section 3: Data Cleaning

Once the stage of data mining has been completed to its maximum potential and productivity levels, the third stage of the lifecycle of data science is one of data cleaning. 

Data cleaning involves, as one might expect, removing any data and information that does not belong in your overall collection of essential data. It is an incredibly valuable aspect of data science, which can substantially improve the efficiency of a company, as well as saving valuable time. 

There are five major steps in ensuring that the data cleaning is as specific and as accurate as possible, which consist of the following:
  1. The removal of all unwanted and unnecessary observations: This is one of the fundamental goals of data cleaning and consists of removing two types of data: data which has been duplicated; and irrelevant data. 
  2. The mending of the basic structure of the data: The next important step in data cleaning is to ensure that the remaining data observations are well structured and that no errors have occurred during the initial transfer.
  3. The filtering of outliers: Essentially, outliers are data points which differ significantly from other data observations within a particular data set. They can be somewhat difficult to identify and should be treated with accuracy and caution.
  4. The dropping of missing values: Dropping the missing values within your data sets means the eliminating the data sets that have one or more pieces of data missing. 
  5. The inputting of missing values: Conversely, you must always indicate missing values within one or more of your data sets, so as to ensure that the information gleaned from analyzing the data is accurate.

Section 4: Data Exploration

Section four of the lifecycle of data science is an exceedingly important one, and essentially it deals with the detailed exploration of the data collated. 
Broadly speaking, data exploration is the middle step in reviewing and analyzing data, and concerns itself with exploring and reviewing large amounts of data and data sets in order to ascertain certain characteristics, patterns, and other points of interest. 
Techniques involved in data exploration include, but are not limited to, the following:
  1. Manual analysis
  2. The use of automatic data exploration and analysis software
  3. Looking closely at the structures of each data set
  4. Identifying why and how outliers are present
  5. The distribution of the data along a certain point in the data set
Data exploration is an incredibly important part of the lifecycle of data science, and computers and software programs are substantially more adept at identifying numerical patterns and cues in data than humans are. Data exploration affords data scientists and data analysts the ability to more accurately ascertain both relationships between data sets and present anomalies in the collection of the data—both of which may pass entirely undetected if not for data exploration. 

Section 5: Feature Engineering

An incredibly important part of the lifecycle of data science, not to mention an extremely effective and beneficial one, is that of feature engineering. 
Feature engineering is the process of transforming and selecting the most relevant and useful variables of data from the raw data collected by utilizing domain knowledge. Feature engineering is used when creating a statistical model or when using machine-based learning, and the main purpose of feature engineering is to drastically improve the overall performance and function of algorithms relating to machine learning. 

When using feature engineering in the lifecycle of data science, there are four main variables which are most effective and accurate when creating a successful and functional machine learning algorithm:
  1. Feature creation: The process of identifying and marking out the specific data variables that can be used most effectively in the planned predictive model. Feature creation requires both the input of computers and the input of human creativity and intervention, and uses existing data features to create derived, entirely new data features by using ratio, addition, multiplication, and subtraction methods. 
  2. Feature extraction: This is the process of extracting new variables from the existing raw data sets; and is incredibly important in the process of automatically decreasing the sheer volume of collected data to enable the data left to be managed and analyzed more effectively. 
  3. Feature selection: Feature selection uses algorithms within the data set which rank, analyze, and judge a variety of different features within the data itself in order to identify which data features are important and therefore should remain; and conversely, which features are entirely redundant and therefore should be removed from the data collection moving forward. In addition, feature selection is an excellent tool for identifying which features will be the most useful moving forward, and therefore should be highly prioritized as the data scientist moves into the next stage. 
  4. Feature transformations: This essentially involves the process of manipulating the variables which have been predicted to improve the overall performance of the model, by ensuring that the model is entirely flexible and capable of varying the data and information it consumes. 

Section 6: Predictive Modeling

The sixth incredibly important and functional process in the lifecycle of data science and analysis is that if predictive modeling. 

Essentially, the process of predictive modeling involves the creation, testing, and validation of a model to most accurately and succinctly estimate the probability of a certain outcome within the data findings that have been projected. 

Data scientists utilize a wide variety of tools and techniques in predictive modeling, including the use of predictive analytics software solutions, statistics, and other mathematical functions such as calculus and algebra—as well as artificial intelligence—to achieve their desired results. 

The process of predictive modeling always follows a strict set of steps, which are as follows:
  1. Pre-processing of data
  2. Data mining
  3. The validation of results found
  4. The understanding of the business and relevance of the data
  5. The preparation of the data
  6. The modeling of the data
  7. Evaluation
  8. Deployment
Statistical analysis, especially throughout the stage of predictive modeling, relies on various specific computer algorithms to accurately produce results.

Time series algorithms—such as single-, double-, and triple-exponential smoothing—are used to perform accurate time-based estimations; association algorithms are used to identify patterns in larger data sets that are transactional in nature in order to produce associated rules; and decision tree algorithms are used to predict and classify one or numerous discreet data variables. 

Section 7: Data Visualization

The final and most crucial part of the lifecycle of data science is that of data visualization. 
Data visualization is the accurate and succinct representation of information and data using visual aids and commonly used graphics, including animations, plots, charts, and infographics. The complex relationships between the data sets can be communicated effectively and simply through the use of such visual representative tools, and can convey all the insights into the data and information found in a concise and easy-to-understand way to other members of the business. 

The primary types of data visualization tools that are used consistently throughout businesses in a wide spectrum of industries include:
  1. Line graphs and area charts: Line graphs and area charts can easily show changes in one quantity (or more than one quantity) by simply plotting a series of information and data points over a certain period of time. Line graphs can demonstrate such adaptions and changes in a clear and concise way, while area charts can connect such changes using line segments. Additionally, area charts are fantastic for visually showing a group of different variables at the same time, using different colors for each variable. 
  2. Tables: Rows and columns in tables are a simple yet incredibly effective way of showing the results of data scientists’ findings, and are useful for showing a great deal of information in one visual aid. 
  3. Histograms: Histograms are graphs which use bar charts, with no spaces separating each bar, to plot a large distribution of quantities and numbers that are categorized within a particular range of data. 
  4. Tree maps: Data scientists and analysts often utilize tree maps when conveying the findings of their work to members of management teams in business settings. Tree maps clearly display data in a hierarchical format and show each group of data collected in rectangular nested shapes. Tree maps are incredibly useful when using the size of the area to compare against other area sizes, and can convey the proportions of each data set found extremely effectively. 
  5. Scatter plots: The final visual aid when working with data visualization, and the one that is most commonly used by data scientists, is that of scatter plots. Essentially, scatter plots are visual aids that can reveal the relationship between two differing variables, and are most often utilized when displaying the findings of regression data analysis.