Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. It lets you:
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In practice, data science is already helping the airline industry predict disruptions in travel to alleviate the pain for both airlines and passengers. With the help of data science, airlines can optimize operations in many ways, including:
In another example, let’s say you want to buy new furniture for your office. When looking online for the best option and deal, you should answer some critical questions before making your decision.
Using this sample decision tree, you can narrow down your selection to a few websites and, ultimately, make a more informed final decision.
Business intelligence is a combination of the strategies and technologies used for the analysis of business data/information. Like data science, it can provide historical, current, and predictive views of business operations. However, there are some key differences.
Business Intelligence |
Data Science |
---|---|
Uses structured data |
Uses both structured and unstructured data |
Analytical in nature - provides a historical report of the data |
Scientific in nature - perform an in-depth statistical analysis on the data |
Use of basic statistics with emphasis on visualization (dashboards, reports) |
Leverages more sophisticated statistical and predictive analysis and machine learning (ML) |
Compares historical data to current data to identify trends |
Combines historical and current data to predict future performance and outcomes |
Watch out this video to know about the exciting field of Data Science.
Machine learning is the backbone of data science. Data Scientists need to have a solid grasp on ML in addition to basic knowledge of statistics.
Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of ML and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.
Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.
Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.
A capable data scientist, you need to understand how databases work, how to manage them, and how to extract data from them.
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Field |
Skills |
Tools |
---|---|---|
Data Analysis |
R, Python, Statistics |
SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner |
Data Warehousing |
ETL, SQL, Hadoop, Apache Spark, |
Informatica/ Talend, AWS Redshift |
Data Visualization |
R, Python libraries |
Jupyter, Tableau, Cognos, RAW |
Machine Learning |
Python, Algebra, ML Algorithms, Statistics |
Spark MLib, Mahout, Azure ML studio |
A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:
The most basic and essential ML algorithms a data scientist use include:
Regression is an ML algorithm based on supervised learning techniques. The output of regression is a real or continuous value. For example, predicting the temperature of a room.
Clustering is an ML algorithm based on unsupervised learning techniques. It works on a set on unlabeled data points and groups each data point into a cluster.
A decision tree refers to a supervised learning method used primarily for classification. The algorithm classifies the various inputs according to a specific parameter. The most significant advantage of a decision tree is that it is easy to understand, and it clearly shows the reason for its classification.
Support vector machines (SVMs) is also a supervised learning method used primarily for classification. SVMs can perform both linear and non-linear classifications.
Naive Bayes is a statistical probability-based classification method best used for binary and multi-class classification problems.
The first phase of a data science project is the concept study. The goal of this step is to understand the problem by performing a study of the business model.
For example, let’s say you are trying to predict the price of a 1.35-carat diamond. In this case, you need to understand the terminology used in the industry and the business problem, and then collect enough relevant data about the industry.
Since raw data may not be usable, data preparation is the most crucial aspect of the data science lifecycle. A data scientist must first examine the data to identify any gaps or data that do not add any value. During this process, you must go through several steps, including:
After you have cleaned up the data, you must choose a suitable model. The model you want must match the nature of the problem — is it a regression problem, or a classification one? This step also involves an Exploratory Data Analysis (EDA) to provide a more in-depth analysis of the data and understand the relationship between the variables. Some techniques used for EDA are histograms, box plots, trend analysis, etc.
Using these techniques, we can quickly discover that the relationship between a carat and the price of a diamond is linear.
Then, split the information into training and testing data—training data to train the model, and testing data to validate the model. If the testing is not accurate, you will need to retrain the model the process or use another model. If it is valid, you can put it into production.
The various tools used for model planning are:
The next step in the lifecycle is to build the model. Using various analytical tools and techniques, you can manipulate the data with the goal of ‘discovering’ useful information.
In this case, we want to predict the price of a 1.35-carat diamond. Using the pricing data we have, we can plug it into a linear regression model to predict the price of a 1.35-carat diamond.
Linear regression describes the relation between 2 variables — X and Y. After the regression line is drawn, we can predict a Y value for an input X value using the formula:
Y = mX + c
where,
m = Slope of the line
c = y-intercept
If you can validate that the model is working correctly, then you can go to the next level—production. If not, you need to retrain the model with more data or use a newer model or algorithm, and then repeat the process. You can quickly build models using Python packages from libraries like Pandas, Matplotlib, or NumPy.
The next step is to get the key findings of the study and convey those to the stakeholders. A good scientist should be able to communicate his findings to a business-minded audience, including details about the steps taken to solve the problem.
Once all parties accept the findings, they get initiated. In this phase, the stakeholders also get the final reports, code, and technical documents.
The demand for data scientists is massive, but the supply is insufficient. With millions of worldwide job openings, the role of a data scientist has become one of the hottest jobs of the decade. While data science is present in all industries, the demand for data science is exceptionally high in the technology, marketing, finance, healthcare, and gaming industries. To know more about the career options available in data science, check out this article on How to build a career in data science and consider enrolling for the Data Science Certification Training Course.
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Name | Date | Place | |
---|---|---|---|
Data Science Certification Training - R Programming | 27 Sep -26 Oct 2019, Weekdays batch | Your City | View Details |
Data Science Certification Training - R Programming | 28 Sep -27 Oct 2019, Weekend batch | New York City | View Details |
Data Science Certification Training - R Programming | 4 Oct -2 Nov 2019, Weekdays batch | Atlanta | View Details |
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