What are the topics covered in a Data Science training course?

By definition, a Data Science training course is designed for learners (recent graduates or professionals changing careers) wishing to acquire the essential knowledge and skills to work in the field of Data Science. Briefly, it is a specific learning process for future Data Science experts. At the end of the training, they will know how to collect, organize, analyze, and interpret large amounts of data. But to achieve this, they must address various topics during the training in order to be able to extract valuable information from the data and deduce actionable insights. In this article, we will detail these subjects that are at the heart of a Data Science training course.

Mathematics and Statistics

Mathematics provides the necessary theoretical foundations for understanding the key concepts of Data Science. Notions of matrix calculation, linear algebra, differential calculus, and probabilities are widely used in algorithms and models.

As for statistics, they allow summarizing and describing the essential characteristics of a data set, such as the mean, median, variance, standard deviation, etc. These statistical measures provide a basic understanding of the data before applying more advanced techniques. They also bring important concepts in Data Science such as statistical inference which allows drawing conclusions from a data sample. There is also statistical modeling which, in Data Science, allows creating models helping to identify the relationships between variables and deducing predictions.

Machine Learning

This is an introduction to machine learning, types of algorithms (regression, classification, clustering, etc.), model evaluation, optimization techniques, etc. Deep learning methods are also covered, as this is a Data Science course that covers Deep Learning, a key essential domain in a data expert’s skills. 

Machine Learning is linked to Data Science on several points:

• The analysis and exploration of data, as Machine Learning allows identifying hidden relationships and trends in these data.
• The preparation of data, as it is important that the data is prepared by cleaning, transforming, and normalizing it.
• Machine Learning algorithms, as they are part of the tools in the hands of Data Science experts, particularly Data Scientists. There are different versions for each type of problem to be solved and data characteristics.

Programming

The programming languages commonly used in Data Science are Python and R. However, programming knowledge covers a much wider field, including the use of popular libraries and frameworks in Data Science such as NumPy, Pandas, TensorFlow, Scikit-Learn, etc.

Other areas are also affected by programming mastery in Data Science, such as data exploration and visualization using visualization libraries like Matplotlib, Ploty, and Seaborn. Other aspects include the construction of Machine Learning models, automating repetitive tasks for efficiency (e.g., data cleaning automation), and the integration of tools and workflows.

Databases

The training provides essential concepts such as the basics of relational databases, SQL (Structured Query Language), data manipulation and management, NoSQL databases, etc.

Mastering these concepts allows working on the following steps:

• The structured and organized storage of data
• Data manipulation using SQL languages
• The integration of data from numerous sources
• The cleaning and preparation of data by eliminating missing values, duplicates, and inconsistencies
• Quick access to data through their indexing and optimization for quick queries
• Real-time data management, which is indispensable when managing streams on social media

Exploration and Visualization

The course provides the knowledge and mastery of data exploration techniques, data cleaning and preparation, data visualization using libraries like Matplotlib or Seaborn, and tools such as Tableau and Power BI.

Predictive Analysis

The mastery of advanced predictive analysis techniques, time series, forecasting models, regression models… is essential.

Moreover,  predictive analysis involves the use of statistical models and machine learning algorithms to make predictions. Data Science learners must therefore be able to select appropriate models depending on the problem and the characteristics of the data. This may include regression models, decision trees, neural networks, support vector machines (SVMs), ensemble methods (random forest, boosting), etc.

Big Data

Last but not least, the learner’s knowledge of Big Data concepts, tools and frameworks for processing massive data (Hadoop, Spark), parallelization techniques, etc., will be a major asset.