Exploring Data Science with Python and SQL

1. EXPLORING DATA SCIENCE WITH PYTHON AND SQL Page 1

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3. Today, Data Science has become one of the hottest professions in the IT world. The demand for qualified and talented data scientists who can help in data-driven decisions has reached exponential heights. The data scientist must have the fundamental knowledge and skill set in open source tools, libraries, SQL, Python, Statistical Analysis, and relational databases. Handling the real-world data sets means learning these data science prerequisites. Apart from theoretical knowledge, you will also require practical knowledge and some experience to dig deeper into data science and start working on advanced projects. Learning Python is regarded as one of the fundamental skills required for a career in data science and as a Python developer. However, Python was not always the most preferred programming language for data science. In fact, R was used in the majority of the data science projects. However, in 2018, Python became the most widely used language for data science and data analytics. This trend of using Python in data science has also given rise to constant development in the Python ecosystem. The journey to becoming a data science professional begins by learning the Python programming language. However, learning Py Python has become really easy with Python certificate courses from educational institutions that provide practical training. 1 Page 3

4. Python certification courses can help you prepare for a data science career. It will challenge and encourage you to write useful code and utilise real-world data to work in data science projects. Apart from learning Python, you will also need to develop a couple of soft skills to become a python developer and data science professional. Learning the Python basics is super important to get a clear introduction to the world of data science. Joining a Python community is a great idea to learn about the plethora of Python Hands-on learning brings the best learning experience to Python students. Continuously practising with mini Python projects will polish your skills and won’t take much time from your schedule. Plus, you will be prepared to handle complex data science projects in the near future. You can start building simple apps and games to familiarise yourself with the fundamental concepts of Python and solidify your understanding of the basics. Python has the three most important libraries meant for data science. The three libraries are MumPy, Pandas, and Matplotlib. Among these libraries, Mumpy and Pandas are the most preferred libraries for exploring and playing with data. Whereas, Matplotlib is a data visualisation library that graphs similar graphs in Google Sheets and Excel. Your professional portfolio should also evolve along with your learning journey. It should have interesting insights into the projects that you have worked on. It should include data cleaning projects, data visualisation projects, and machine learning projects. Finally, when you have properly learned the Python programming language, you will need to explore some of the advanced concepts to become a data science professional. It is important that you are well acquainted with regression, clustering models, and classification. You can learn machine learning, bootstrap, and neural networks with scikit-learn as well. 2 Page 4

5. There is a lot of concern about this question, especially among data science enthusiasts. Usually, an average individual requires a training period of three months to six months with consistent practice to become a Python developer. However, it depends on your willpower and free time that you can dedicate Data science professionals can use Python or R for data science projects. Both of the languages have their own strengths and weaknesses. However, Python is given more preference among professionals for its simpler syntax. R is usually preferred in academia and research industries. Being a data science professional means that you have to learn at le least one of these languages. It is also advisable to consider enrolling on a data analytics certification course to learn faster. SQL is also known as one of the most prominent skills in Data Science. It is mainly a set of instructions commonly used for interacting with a relational database. This interaction might include capturing/saving/managing the data or querying the data. SQL enables the developers and data scientists to play around with large data sets. Today, it has become an important tool for data scientists in accessing, inserting, updating, manipulating, and modifying data. Unlike other programming languages with complex syntax and the need for memorisation of steps to execute a task, SQL is known for its simplicity. You will love how it uses a simple language structure in English that is very easy to understand. If you are thinking about starting a career in data science, then SQL is the perfect language to learn in becoming a data science professional. The shorter syntax can help you query data easily and get insights from it. It is widely believed that SQL is one of the foundation stones of data science. 3 Page 5

6. As a data science professional, you are required to work with huge sets of data and have a clear idea about its nature. Having knowledge of SQL will greatly help you understand relational databases. You will be able to carefully investigate the dataset, visualise it, understand the structure, and learn how exactly your dataset looks. This makes it possible to find out about missing values, recognise outliners, NULLs and the exact format of the dataset. SQL assists the data scientists to conduct proper incisions to understand the data carefully. Despite so many advantages of using SQL, it is still very limited to visualisation. You will need an additional scripting language to perfectly visualise your data. Thankfully, SQL perfectly integrates with scripting languages right Python, or R. SQL and Python makes a perfect combination to work with data science projects. Dealing in data science means working with huge datasets saved in relational databases. This means it is not practically possible to work with such huge data in your usual spreadsheets. We require high-level solutions like SQL to deal with huge and complex datasets. When using SQL, we don’t have to worry about pools of data in relational databases. It can easily communicate and gather meaningful insights from the data. 4 Page 6

7. If you want to become a data scientist, then the majority of the employers will demand you have proficiency in SQL. Expertise in SQL enables professionals to solve complex problems in projects. SQL skills help you store, update, and manipulate datasets to become a successful data science professional. SQL is also known for its perfect in integration with database management systems such as Microsoft SQL Server, MySQL, dBase, etc. The professionals are able to switch among systems with the help of this integration. Therefore, learning SQL can help you master data science and stay ahead of the competition. Every aspiring data scientist should consider learning SQL before applying for data science jobs. It will give you a great understanding of datasets, the ability to integrate with other scripting languages, the ability to handle huge data sets, and excel in a data science career. There’s a famous saying that ‘data is the new oil’ . All the top organisations are looking for data science professionals to draw meaningful insights from huge data sets and provide them with a competitive edge in the market. The London School of Emerging Technology provides the best data science certification and data analytics certification courses with a practical learning environment. We teach data science in a project-based curriculum that empowers students to work with real-world data science projects. We also provide Python certification courses for aspiring data scientists. 5 Page 7

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