Essential Data Science Topics to Real World Applications

What is Data Science
} Data Science is the science which uses computer science, statistics and machine learning, visualization and human-computer interactions to collect, clean, integrate, analyze, and visualize data.

} Goal of Data Science : Turn data into data products.

Data Science – Visual Definition

Data Science – Why Excitement
} Data Science is currently a popular topic in industry due to its applications in:

} Quantitative decision making and control.

} Identifying risks, opportunities using ‘what-if’ analysis.

} Predictive Analytics

Data Science – Key Topics
} Data Visualization: Finding patterns in Data.
vLiner Trend
vClusters etc.,

} Data Analysis (Exploratory Analysis): Understand Empirical Properties. Statistical Methods are used.
vTime Series
vCorrelation
vANOVA

} Data Analytics (Predictive Analysis): Forecast Future Data Values.
vMachine Learning

Data Visualization
} Data visualization is defined as a graphical representation that contains the information and the data.
} By using visual elements like charts, graphs, and maps, data visualization techniques provide an accessible way to see and understand trends, outliers, and patterns in data.
} Data visualization is another technique of visual art that grabs our interest and keeps our main focus on the message captured with the help of eyes.
} Whenever we visualize a chart, we quickly identify the trends and outliers present in the dataset.

Data Visualization Diagrams

Benefits of Data Visualization
} Powerful technique to explore the data with presentable and interpretable results.
} Supports the data cleaning process by finding incorrect data and corrupted or missing values.
} Helps to determine the patterns of data distribution.
} Storytelling and decision making is made easier.

Data Visualization Tools

} MS-Excel is the simple tool to create charts from 2-dimensional table.

} Tableau is one of the leaders in this field. It is a Business Intelligence (BI) tool.

} Plotly is one of the most popular plug-in to create charts using R or Python

Effective Data Visualization Tips

} Right Choice of Visualization: No visualization is one-size-fits-all. It’s crucial to choose the right visualization technique for each type of data on a dashboard to ensure its usability and to avoid confusion or misinterpretation.

} Consistency: Stick to specific color-coding, fonts, styles and visualization elements when showing the same metrics across different dashboards.

} Personalization: Keep the goals of different end-users in mind when deciding what visualizations and data should be included in a dashboard. Important information for one user may be unessential or for the others.

Data Analysis
} Exploratory Analysis, which answers the question, ‘What happened?’

} Exploratory Analysis is the type of data analysis that helps describe, show or summarize data points in a constructive way and identify patterns that fulfill every condition of the data.

} Mainly statistical analysis. It gives you a conclusion of the distribution of data, helps you detect errors and outliers. Enables you to identify similarities among variables, thus making you ready for arriving at statistical conclusion.

Statistical Methods

} Analysis of Variance (ANOVA) tells you if there are any statistical differences between the means of three or more independent groups (such as age, gender, income).
} Factor analysis is a way to condense the data in many variables into a just a few variables. For this reason, it is also sometimes called ‘dimension reduction’. You can reduce the ‘dimensions’ of your data into one or more ‘super-variables’. The most common technique is known as Principal Component Analysis (PCA).
Advantages of Data Analysis
} This type of study gives the researcher the flexibility to use both quantitative and qualitative data in order to discover the properties of the population.
} This analysis is very real and close to humanity as all the trends are made after research about the real-life behavior of the data.
} Data analysis gives you a conclusion of the distribution of data, helps you detect outliers, and enables you to identify similarities among variables, thus making you ready for conducting further statistical analyses.
} Helps us to choose the right machine learning model for predictive analytics.
Tools for Data Analysis
} R Programming
} Tableau
} SAS: Modules for web, social media and marketing analytics included.
} SPSS
} MatLab
Data Analytics
} Applying data patterns towards effective decision-making.
} Analytics is the systematic computational analysis of data or statistics.
} Predictive analytics encompasses a variety of statistical techniques and machine learning that analyze current and historical facts to make predictions about future.
} Predictive analytics is used in marketing, retail, capacity planning, social networking etc.
What is Machine Learning
} Machine learning (ML) is a type of Artificial Intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
} Machine learning algorithms use historical data as input to predict new output values.

} Machine learning enables models to train on data sets before being deployed.
} This iterative process of training leads to an improvement in the types of associations between data elements.
} After a model has been trained, it can be used in real time to predict future values.

Supervised Learning Algorithms
} Supervised learning is a type of machine learning in which machines are trained using well ‘labeled’ training data, and on basis of that data, machines predict the output.
} The training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly.
} It applies the same concept as a student learns under the supervision of the teacher.