I highly recommend John Foreman's book: 'Data Smart - Using Data Science to Transform Data into Insight'. The author's approach is unique - he teaches data science skills without teaching programming. His approach works because he limits the newness of each subject item to one dimension, that being the data science technique at hand. Each skill is introduced in the familiar environment of Excel and its spreadsheets. If the author had introduced a programming language at the same time, the difficulty of staying focused on the technique being taught would have been increased greatly.Each technique introduced is explained by first posing and then working through a business problem. Each problem has an accompanying Excel dataset. For each challenge, you are soon immersed in data - your Excel skills will be stretched and improved and you will need to work hard to keep abreast of each lesson. It is worthwhile for the reader because tangible learning occurs as the data is worked through and the business challenges are demonstrably solved.The techniques you will learn include:Cluster analysis using k-means and k-medians algorithmsDocument classification using a Naive Bayes algorithmOptimization modeling using linear programming methodsCommunity detection in network graphs using modularity maximizationLinear and logistic regressionEnsemble models based on bagging and boosting techniquesForecasting based on exponential smoothing with trend and seasonality (ETS)Outlier detection using k-nearest-neighbors and local outlier factorsIn addition to teaching techniques, the author sprinkles the text with conclusions he has formed based on his years of data science practice and on a healthy skepticism for some of the overblown claims made for data science. His advice includes the following:Beware the poorly posed problem. Data scientists must learn to communicate with stakeholders in order to help frame business problems properly.You will generally get more benefit by spending time selecting good data and engineering better data features than searching for a marginally better predictive model. A great model cannot deliver good performance with poor data.Remember, industry is not academia. In industry, analytics is a results-driven pursuit. The best model is the one that strikes a balance between performance and usability. If a model is never used, it is worthless.Mailchimp, the author's employer, has TB of data under management. However, it neither needs nor uses all this data for analysis and model training. Instead it aggregates its data to the level the business requires. This aggregate dataset is typically 10GB in size. This aggregation enables mailchimp to use R in production.Data science is not the most important function in the organization. The purpose of the data science function is to serve the business and help it achieve its goals. The purpose of data science is not to build models for their own sake.The author introduces the R programming language in the final chapter, as a gateway to doing further work in data science. R is very powerful but it can appear to be a black-box tool for solving data analysis problems. As the author explains, R has great performance but it is not great for explaining the inner workings of models.At the outset, the author explained that he preferred clarity over mathematical correctness when writing the book. He did an excellent job. The aspiring data scientist will learn by doing in the familiar and manageable environment of Excel.