Day 3: Getting Started with Statistics for Analytics - Analytics Extra Mentorship Program 1.0

Session by: Ajiboye Yusuf

The session introduced the fundamentals of statistics for data analysis. Mr. Ajiboye Yusuf emphasizes the importance of statistics in understanding and interpreting data, particularly in the context of data-driven decision-making.

Key points from the Session:

• Statistics is a branch of mathematics that deals with collecting, organizing, analyzing, and visualizing data to draw conclusions and solve problems.

There are two main types of statistics: descriptive statistics and inferential statistics.

1. Descriptive Statistics: are used to summarize and describe data sets using both visual and numerical methods. Visual summarization involves the use of graphs and charts whereas Numerical summarization involves the use of numbers (measure of central Tendency/location and measure of dispersion/spread) to describe the dataset. Measure of Central tendency commonly uses points such as the Mean, Median and mode with the median being a preferred measure because of its non-sensitivity to outliers. Measure of Dispersion/spread uses points of measure such as Mean Absolute Deviation, Standard Deviation, Variance and Range with the Standard Deviation (SD) being the most commonly used measure.
2. Inferential Statistics: are used to make inference (draw conclusions) about a population based on a sample of data. The basis of inferential statistics is that a sample is collected from a population and is used to make inference about the data.
3. Probability: It is a measure of the likelihood that an event will occur. Probability is important for Data Analysts for them understand and analyze the data. This includes a) Additive & Multiplicative rule b) Dependent & independent events c) Mutually exclusive & Inclusive events d) Conditional probability & Bayes' theorem.
4. Data: Any useful information that has been collected (Overtime) regarding a group of people or thing either from a population or sample. Types of data include; categorical/qualitative data & numerical/quantitative data

• Data cleaning and preparation are important steps before data analysis can begin. Without data collection, data cleaning and data analysis, data visualization is unnecessary.
• Data analysis involves applying statistical techniques and data mining to extract meaningful information. Data visualization techniques are used to communicate insights effectively.