![]() Getting information about the variables involved and the potential relationships between those variables is the first step in using the data for analysis.Ĭentral tendency assists data scientists in measuring the median values of a dataset, thereby helping them navigate the central location of data to focus on. Descriptive statistics is important because it helps data scientists understand the data they are dealing with in a comprehensive manner. The tool measures central tendency, dispersion and distribution of data using statistical techniques.ĭata scientists can use this tool for summarising and describing their dataset and enhance their exploratory data analysis by describing the characteristics of the data. Let’s look at these tools in some detail.ĭescriptive statistics essentially entails the measure of central tendency and dispersion. Depending upon their use case, data scientists can choose among the four tools or combine them for better results. There are four broad categories of statistical tools that can be used, depending on the organisation’s use case, their product/service offerings, and the kind of insight they aim to derive from their data. For instance, CPG organisation leaders can use econometrics to optimise the promotional spend and ROI for a market and use econometric & statistical tools to quantify the relationship and draw conclusions.ĭelhivery Plans to Hire 75,000 Staff to Meet Festive Rush This combination of data with the quantitative application of statistical and mathematical models helps data scientists to test existing hypotheses and forecast future trends.ĭata scientists in CPG industries can easily adapt econometrics, given their deep understanding of the maths behind linear regression or panel data analysis techniques. The latest AI-based techniques and predictive economic modelling tools help organisations systematically identify the economic factors that can influence their business decisions. They allow organisations to utilise quantitative methods to test data-driven theories in a real-life scenario. This is especially true when it comes to industries like CPG that are so volatile to external factors. Statistical tools are essential for data analysis. Data scientists tend to lack the in-depth knowledge in statistics that could further their insight generation.
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