Data science tools are application software or frameworks that help professionals perform various tasks like analysis, cleaning, visualization, and mining. They also cater to different data types and formats.
Tableau is one of the top-level data visualization and Business Intelligence (BI) tools that many MNCs use to gain insights from their data. It is a popular open-source software that allows you to create interactive visualizations.
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1. Data Cleaning
Often overlooked, data cleaning can be one of the most important things you do as a data science professional. Poor quality data can skew your results and prevent you from finding valuable insights in your analysis.
It is vital to have good data cleaning practices, especially if you are going to be running your data through machine learning algorithms. Unclean data can lead to misleading or inaccurate results that could have a major impact on your business.
In this course, you will learn how to use the core tools in a data scientist’s toolbox. You will get hands-on experience using version control and reproducible electronic reporting (e.g. GitHub and RStudio), and the Python programming language. You will also gain familiarity with MATLAB, which is widely used by data scientists for statistics, data manipulation and visualizations.
2. Exploratory Data Analysis (EDA)
EDA is a necessary step to ensure that the results data scientists produce are valid, correctly interpreted, and applicable to desired business contexts. It allows data scientists to identify obvious errors, better understand patterns within the data sets, and find interesting relationships between variables.
It also helps to make sure that the model being built is appropriate for the problem at hand. Many data professionals skip the EDA process or do a mediocre job of it, which can lead to inaccurate models and sub-optimal performance.
Some examples of EDA techniques include displaying quantitative data in a shortened format using stem-and-leaf plots (grouping data points by their leading or trailing digits), presenting patterns through the use of line graphs, scatter plots and heat maps, and identifying outliers and anomalies with statistical methods.
3. Data Visualization
Data scientists often work with various programming languages and tools to prepare data for analysis. They also use software to create and display visual representations of their data.
Having the right data visualization techniques in your toolbox is important for making it easier to communicate results and insights to others. It can help you make information more accessible to your teammates and clients, so they can better understand the performance indicators that matter most to them.
Some of the most common visualization tools include line graphs, scatter plot charts, mekko charts, stacked bar charts, dual-axis charts, bubble chart, and a heat map. These methods can save you time as they allow you to easily identify trends, peaks, and troughs in your data. Then, you can take action to improve your company’s performance and increase profitability.
4. Predictive Modeling
Data Science consists of techniques for extracting knowledge from data and using this to build smart decision-making systems and automation. This involves advanced analytics methods such as machine learning modeling.
Predictive analyses attempt to use current and historic data to predict future events. This can include classification, regression and clustering.
For example, a supermarket could use association rule learning to determine which products are frequently bought together (market basket analysis). Similarly, clustering is used to divide data into similar groups to identify anomalous or unusual patterns such as finding correlations between spelling bee winners and the number of people killed by venomous spiders.
5. Data Mining
Data mining involves a wide array of computational theo- ries and tools to assist humans in extracting useful information (knowledge) from rapidly growing volumes of digital data. Various techniques for performing data-mining operations include summarization, classification, regression, clustering and more.
Companies can use this information to tailor their product offerings and customer service to meet their customers’ needs, increasing sales, and creating a more personalized customer experience overall. For example, a company could create a profile of its most valuable customers that includes their interests, purchase history and behaviors, which can then be used to provide them with targeted offers and promotions.
The Data Science Toolbox course provides an introduction to the main tools and ideas in a data scientist’s toolkit. It also gives a practical introduction to the tools that will be used throughout the program like version control, markdown, git, GitHub, and RStudio.
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