What Is Data Science?
Data science is the study of data-driven techniques, models, and theories. The people who use data to find new insights, interpret quantitative information, inform decisions, and give predictions.
Data science is an interdisciplinary subject that incorporates aspects of computer science, statistics, cognitive science, natural sciences (such as biology or physics), social sciences (such as sociology or political science), anthropology, and mathematics on many levels. A person needs to be well versed in all these fields to contribute effectively towards data science.
How Does Data Science Work?
Data science is a field of study that deals with collecting and analyzing data such as demographics, preferences, interests, etc. It is done to glean insights for better decision-making. Data scientists either work on their own or form teams for this analysis. As such, some of the tools they use are:
– Statistical models: This help distinguishing between correlation and causation and establish relationship among variables.
Available statistical tests include:
– One sample test: For assessing the distribution of a single variable
– Two sample tests: For assessing the distribution of two related variables, one treated as the dependent variable and the other independent variable
– Chi-square test: This is an extension of the two-sample test
– ANOVA test: This is related to the two-sample test, but it uses more than two associated variables
– Bivariate correlation: Used for assessing the relationship between two variables
– Linear regression: Assesses the relationship between an independent variable and dependent variable. The trend of this is plotted to help predict future trends.
– Data mining methods: Used for finding relationships or patterns in data
– Machine learning algorithms: These are used to create predictive models. Some of the most common ones are:
- Decision trees
- Random forest
- Naïve Bayes classifier
- Support Vector Machine (SVM)
These can be used in conjunction with machine learning methods to refine the results. Also, some of these models are used only for specific purposes, such as decision trees for predictive analysis and SVM for classification.
– Artificial intelligence (AI) techniques: Used in algorithms that mimic human intelligence
Data Scientists can use tools like R or Python, open-source programming languages used for statistical computing, to analyze data. However, this is not the only thing that needs to be done by a data scientist. Before all of this can happen, there needs to be an effective plan defined to work correctly.
This includes tasks like understanding what problem needs solving, finding out how much data is available, deciding what information is relevant, how it can be obtained, etc. This is called the pre-processing stage of the project. Once this is done, modeling techniques are used to analyze and represent data in an accurate manner.
Data Scientists perform testing to check whether the model they have made fits into the bigger scheme of things. Once this is done, a relevant model can be made and then implemented to get an outcome.
Everyday Tasks of a Data Scientist
- Data collection: This step involves collecting the data needed for analysis
- Data exploration: Analyzing information from collected data
- Data preparation: Manipulating or cleaning up the data from data exploration
- Data visualization: Information from the processed data is represented in a visual format which helps get a clearer picture of what is going on
Data Science in Industry
There are different domains where data science can be applied, the most common one financing. Financial firms use this knowledge to mine through large amounts of financial data and ultimately make better strategies that can be implemented in the future.
- Data mining: This is a process where pre-existing but large sets of data are analyzed for trends
Data scientists work with data from various sources that vary from machine data to social media platforms. Financial firms use this information to find patterns that can be applied to make more informed decisions in the future.
Data science is also used in marketing and advertising for companies like Google and Facebook. These platforms can analyze users’ data and provide them with relevant advertisements based on their online activity, making the consumer’s experience more personalized and ultimately improving overall business results.
- Element of automation: This means that data scientists already have set guidelines to make decisions that can be done by the resources they use (computer, artificial intelligence programs, Autonomous Cars etc.)
- Creation of predictive models or algorithms: This is where data science helps produce better decision-making results.
Data Science Tools
The first step towards becoming a good data scientist is understanding the relevant tools that help perform tasks such as data cleaning, analysis, and processing.
- Apache Spark: An open-source framework that has APIs written in different languages such as Java, Scala, and Python
- Cloudera: A distribution of Apache Hadoop, which provides data storage and preparation tools through libraries written in Java and Scala.
- Python: is a general-purpose, high-level language that allows the programmer to focus on the ‘what’ rather than writing code for tedious and repetitive tasks. This language can perform data analysis through libraries such as NumPy, SciPy, and pandas.
- Hadoop: An open-source framework that provides distributed storage and processing through the Map-Reduce paradigm. Hadoop is written in Java and can be used to manage large amounts of unstructured data.
- Hive: is a data warehouse infrastructure built on top of Hadoop to provide data summarization, ad hoc querying, and analysis. It also allows the integration of SQL-like queries with the data stored in Hadoop.
Tools such as Apache Spark, Cloudera, and Python are generally used to perform pre-processing, unstructured data integration, analysis, and visualization.
Data Science Job Opportunities
As discussed, there are multiple ways where data science can be used, but if you are planning on taking up a career path in this area, there are some typical job opportunities you can consider:
- A data scientist at a financial firm where you would be involved in tasks such as mining, data analysis, and creating predictive models.
- People analytics manager or vice president at a company who researches talent management strategies for employees.
- Product manager at an eCommerce platform requires you to understand the tools such as big data, data warehousing, and business analytics to make informed decisions on how the company should operate.
- Data engineer at a start-up where you can assist in tasks such as designing and building an efficient data platform for collecting and storing large amounts of information that can then be used to help with decision making.
In this article, we’ve explored the different tasks that a data scientist performs and some of the tools they use to do so. Data science is an ever-evolving field that continuously adapts to the changing needs of industries and has vast potential for growth in the future.