Foundations of Data Science - Fundamental Concepts and Techniques

Data science is about analyzing and understanding datasets in order to make meaningful interpretations of real-world phenomena. It combines critical concepts and skills in computer programming and inferential statistics with hands-on analysis of real-world datasets.

Breiman (2001) offers a maximalist account that qualifies data science by distinguishing between two broad classes of epistemic products: correlative predictions and information about any associated underlying natural causal mechanisms.

1. Inferential Statistics

Data science uses analytical tools like hypothesis testing, regression analysis and sampling techniques to determine statistical conclusions about the population data from a sample. It uses sampling techniques to create a representative sample from the entire population. It includes the use of analytical tools that measure dispersion, such as mean and standard deviation and probability tests like t test, Z test and linear regression.

The main difference between descriptive statistics and inferential statistics is that descriptive statistics summarize raw data and describe its characteristics while inferential statistics make conclusions and predictions about the larger population using data from a sample. Data science can help you make sense of your business data and answer key questions. It can help you identify patterns and relationships, make predictions and find optimal solutions. It can also provide data visualizations and reporting that allow non-technical business leaders and busy executives to easily understand the information. This helps them make more informed decisions and move towards evidenceaEURbased practice.

2. Machine Learning

A core component of data science, machine learning applies statistical methods to algorithms to teach them how to make classifications and predictions. These algorithms then drive decision making within applications and businesses, ideally impacting key growth metrics.

For example, a common machine learning application is speech recognition that enables familiar technology like virtual assistants and voice-controlled devices. Another is image recognition that lets computers recognize objects in a photograph or video, which can be used in a wide range of applications from security to healthcare.

Data science also encompasses predictive analytics, which forecasts future behavior and events, and prescriptive analytics that aims to recommend the best course of action. It's important for business leaders to understand these advanced analytics techniques to gain insights that can lead to stronger marketing campaigns, more profitable financial trading, better manufacturing uptime and higher cybersecurity protections. This free e-book offers tips and tricks for creating more robust predictive models.

3. Data Visualization

The ability to make sense of data sets can help businesses identify new trends and opportunities. For example, if a business notices a correlation between customer churn and product quality it can use this insight to improve future decisions.

Visualization makes it easier to find patterns, outliers and correlations in large data sets. It can also be helpful during exploratory analysis, when a team is trying to get a lay of the land with a dataset before delving into more detailed analyses.

There are several types of data visualization, including tables, graphs, maps and infographics. For example, a graph may show changes over time by using line or bar charts. A map shows geographical data with different shapes and colors. A Gantt chart is a special type of chart that uses different shapes and lines to represent tasks in a project. Other visualization techniques include box plots and scatter plots. All of these can be used to convey information about a dataset, such as its distribution or whether it’s symmetrical or skewed.

4. Data Management

Data science requires advanced computing, which involves writing and debugging the source code of computer programs. It also utilizes mathematics, which is the study of quantity, structure, space and change. Moreover, it requires data visualization tools and reporting software. Finally, it uses a variety of machine learning algorithms, such as neural networks and high-dimensional geometry.

Effectively managing data requires a process that begins with defining the business questions companies want to answer and ends with acquiring and analyzing the right data. Data management also includes best practices for storing, delivering and protecting data.

A well-defined data strategy can help businesses address their most pressing problems, such as reducing fraud and improving customer service, increasing manufacturing uptime and cybersecurity protections, and making better decisions based on real information instead of hunches. In addition, data management helps ensure a company’s data is reliable and trustworthy. This is achieved by ensuring the right people have access to the right data and that the data can be trusted.