In this chapter, we briefly covered the steps involved in the data science life cycle, such as data acquisition, data preparation, and data exploration through descriptive statistics. We also learnt to estimate the population parameters through sample statistics using some popular tools and techniques.

We explained the basics of statistics from both theoretical and practical aspects by going deeper into the fundamentals in a few areas to be able to solve business problems. Finally, we learnt a few examples on how statistical analysis can be performed on Apache Spark, leveraging the out-of-the-box features, which was basically the objective behind this chapter.

We will discuss more details of the machine learning part of data science in the next chapter as we have already built statistical understanding in this chapter. Learnings from this chapter should help connect to the machine learning algorithms in a more informed way.