Have you ever wondered what a comprehensive syllabus of a data science course would look like? Data scienceData Science is an ever-evolving field with various tools and technologies available for analysis. We will provide an overview of the tools used in modern data science and their related usage in the industry. To start off, having a strong knowledge base about programming languages such as Python and R is essential for most data scientists. These programming languages are used for statistical analysis and other data manipulation tasks. You’ll need to be familiar with object-oriented concept, primitive data types, functions, operators, control flow statements and classes to effectively work with Python and R. Additionally, both languages have extensive libraries that can be utilized to perform different tasks while working with large datasets. Data scientists also rely heavily on machine learning algorithms. Knowledge of supervised learning algorithms such as linear regression, logistic regression and naïve bayes will enable you to apply predictive analytics in practical scenarios. Having a thorough understanding of unsupervised learning algorithms such as clustering and dimensionality reduction will help you create effective models from un-labelled datasets. Apart from the above-mentioned tools and technologies, having hands on experience with databases such as SQL and NoSQL are also important for data scientists. This allows them to understand how different kinds of datastores store information which can be used for further analysis using SQL queries or NoSQL commands. Additionally, being proficient in big data processing frameworks such as Hadoop will help you deploy sophisticated big data solutions using real-time streaming or batch processing techniques. In conclusion, these tools form the foundation of modern data science syllabus giving budding professionals an edge in this highly competitive field.