1. Introduction to data science: This includes an overview of the field, the role of data scientists, and the various tools and technologies used in data science.

2. Data preprocessing: This includes techniques for cleaning, transforming, and preparing data for analysis, such as data cleaning, data normalization, and feature engineering.

3. Exploratory data analysis: This includes techniques for exploring data to understand patterns and relationships, such as data visualization and statistical analysis.

4. Machine learning: This includes an introduction to machine learning algorithms and techniques, such as linear regression, logistic regression, decision trees, and clustering.

5. Deep learning: This includes an introduction to deep learning techniques, such as neural networks and convolutional neural networks.

6. Big data technologies: This includes an introduction to big data technologies, such as Hadoop and Spark, and their role in data science.

7. Data visualization: This includes an introduction to data visualization tools and techniques for creating effective visualizations that communicate insights and findings.

8. Model evaluation and validation: This includes techniques for evaluating and validating machine learning models, such as cross-validation and performance metrics.

9. Data ethics and privacy: This includes an introduction to ethical considerations in data science, such as data privacy, bias, and fairness.

10. Hands-on projects: The training program should also include hands-on projects where participants can apply their knowledge and skills to real-world problems.

How does Data Science works:

1. Identify the problem: Identifying the problem or question that you want to utilise data to solve is the first step. This involves detailing the criteria and constraints of the study, as well as clearly defining the research subject and identifying the relevant variables.

2. Collect and clean the data: The necessary data must next be compiled and cleaned by being free of errors, inconsistencies, and missing values. In this case, data wrangling, preparation, and integration may all be required.

3. Following data cleansing, it must be studied and visualised using statistical techniques and data visualisation tools. This involves summarising the data, identifying patterns and trends, and visualising the data in order to gain insights and identify likely outliers or anomalies.

4. After analysing the data, develop and test prediction models using machine learning techniques. This requires selecting the appropriate model, training it with the data, and then validating the results to determine how well it performed.

5. Communicate the results: The next step is to concisely and clearly explain the investigation's findings. For this, it could be essential to produce visualisations, write reports, and convey the findings to stakeholders.

6. Data science often integrates statistical, mathematical, and computational methods to assess data and provide insights. Many procedures and techniques may be used, depending on the issue being investigated, the type of data being used, and the study's goals.

Future of Data Science:

1.  Use of artificial intelligence and machine learning will increase as data volume continues to rise, necessitating more automated algorithms for data analysis. Artificial intelligence (AI) and machine learning can assist in automating data processing and producing predictions, resulting in more effective and efficient decision-making.

2. An increased focus on data privacy and security: As more data is gathered and processed, an increased focus will be placed on safeguarding data privacy and security. To guarantee that they are following rules and preserving sensitive information, data scientists will need to be knowledgeable on data security and privacy laws.

3. IoT and sensor data growth: As the Internet of Things (IoT) develops, more data is anticipated to be produced by connected devices and sensors. For insights and forecasts, data scientists will need to be able to interpret this data.

4. Demand for data storytelling will rise as data complexity rises, necessitating a growing need for data scientists who can convey their results in an understandable and succinct way. It is anticipated that data storytelling will become an increasingly crucial ability in the industry. Data storytelling is the art of presenting data in a way that is interesting and simple to grasp.

5. Data ethics will become increasingly important as data science's influence on society grows. As a result, data scientists will need to think more carefully about the ethical implications of their work. This will cover topics including algorithmic bias, privacy difficulties, and the ethical use of data.