Basic Data Analytics Concepts

What is the first step in the data analysis process?

Where does internal data typically originate from?

What is the purpose of data cleaning in the data analysis process?

Which of the following is NOT a common step in data cleanup?

What does data visualization aim to do?

Which technique is commonly used in data visualization?

What is the primary goal of data visualization?

Which of the following is a common measure of central tendency in descriptive statistics?

Which of these is not a typical step in the data cleaning process?

Which of the following is a measure of variability in descriptive statistics?

What type of data visualization is typically used to display the distribution of a single variable?

What is the primary purpose of data cleaning in the data analysis process?

Which of the following is a common technique used in data visualization?

Where does external data typically originate from in the data collection process?

What is the primary goal of descriptive statistics in data analysis?

Which of the following is NOT a common step in the data collection process?

Basic Data Analytics

Data Collection

Data collection is the first step in the data analysis process. It involves gathering data from various sources, both internal and external to the organization. Internal data comes from within the business, such as CRM software, internal reports, and archives, while external data originates from outside the company, through surveys, questionnaires, and public data. Software plays a significant role in this phase, with analytics or business dashboard tools and reports from internal tools like CRMs providing internal data. External data is obtained using survey apps and other data collection tools.

Data Cleaning

Once the data is collected, it must be cleaned before analysis begins. Data cleaning, also called data cleansing, is essential to ensure the quality and reliability of the data. It involves checking the data for errors and inconsistencies and correcting or removing them. This step is crucial for obtaining accurate and meaningful results from the analysis. Data cleanup looks different based on the type of data; it may include removing unnecessary information, addressing structural errors, deleting duplicates, trimming whitespace, and verifying human input for accuracy.

Data Visualization

Data visualization is the process of presenting data visually, making it easier to understand and analyze. Graphical representation of data, such as charts and graphs, are common visualization techniques. Data visualization helps to make complex data more comprehensible and provides a clear picture of the findings. Tools like Power BI, Tableau, and Excel are popular for data visualization and presentation purposes.

Descriptive Statistics

Descriptive statistics are used to describe and summarize data. Commonly used descriptive statistics include mean, median, mode, standard deviation, and percentiles. These measures provide an overview of the distribution of the data and help to identify central tendencies, dispersion, and skewness. Understanding descriptive statistics is crucial for interpreting and presenting data accurately.

Learn about essential concepts in data analytics, including data collection, cleaning, visualization, and descriptive statistics. Understand the importance of each step in the data analysis process and how it contributes to deriving meaningful insights from data.