Introduction to Data Science
Ever thought how does How does Facebook automatically tag the faces of recognised individuals? Netflix and Amazon prime recommend videos based on the genre of your choice? Or how the identification of potentially loyal customers and which are most likely to leave for a competitor is done by banks and how has the drug discovery process simplified? All of these “how’s” are possible because of the emergence of Data Science! Blend of machine learning techniques, algorithms, business acumen, and mathematics, Data Science helps to find out the hidden patterns from raw data. This skill becomes instrumental because this information will help the organization make informed and big decisions related to their business.
Let’s Start 1. Data Scientist – Works on complex and specific problems to bring non-linear growth to the corporate. For instance, making a credit risk solution for the banking system or use images of vehicles & assess the damage for an insurance firm automatically.
2. Data Engineer – He/she would implement the outcomes derived by the info scientist in production by using industry best practices. For instance, deploying the machine learning model built for credit risk modelling on banking software.
3. Business Analyst – Helps in running the business smoothly by assisting the management to form data-driven decisions on a day-to-day basis. This role would be communicating with the IT side and therefore the business side simultaneously.
Again, there are tons of other roles under the info science umbrella-like Data Analyst, Statistician, Data Analytics Manager, MIS Professional BI Professional, etc. confirm what you are going for before jumping into this space.
How to start in Data Science? • Getting Started with Data Science and Python: the beginning of your journey to becoming a data scientist is to understand what a data scientist does, the varied terms related to data science, and begin getting familiar with the Python programming language
• Statistics and Mathematics: They both are the backbone of data science and AI. A number of the key concepts you’ll cover are probability, inferential statistics, and obtain a hang of the way to perform Exploratory Data Analysis (EDA). This may also include the fundamentals of algebra (another core machine learning topic)
• Machine Learning Basics: Welcome to the planet of machine learning! This section is all about introducing you to the essential machine learning algorithms and techniques, including Rectilinear Regression, Logistic Regression, Decision Trees, Naive Bayes, Support Vector Machines (SVM), among others
• Assembly Learning: Now is the time to deep dive into advanced machine learning topics. Understand what assembling is, the various ensemble techniques, and begin performing on datasets to realize a hands-on practical experience
As discussed earlier, you'll learn all of this in comprehensive details as a part of the Data Science program.
Tools you want to master for Data Science • Microsoft Excel – Excel prevails because of the easiest and hottest tool for handling small amounts of knowledge. The utmost number of rows it supports is simply a shade over 1 million and one sheet can handle only up to 16,380 columns at a time. These numbers are simply not enough when the quantity of knowledge is big.
• SQL – SQL is still among the foremost popular Data Management Systems which has been around since the 1970s. It had been the first database solution for a couple of decades. Despite of SQL being so popular, there’s a drawback – it becomes difficult to scale it because the database continues to grow and it becomes messy.
• Python – this is often one among the foremost dominant languages for data science within the industry today due to its ease, flexibility, and open-source nature. It has gained rapid popularity and acceptance within the ML community.
• Tableau – it's amongst the foremost popular data visualization tools within the market today. it's capable of handling large amounts of knowledge and even offers Excel-like calculation functions and parameters. Tableau is well-liked due to its neat dashboard and story interface.
Soft Skills for Data Science
For any technical job field, you need to have good soft skills and logical reasoning skills. Some of them required for Data Science are
• Problem-Solving Skills – The knowledge of statistics and computing are often achieved by studying but it's the domain knowledge alongside the problem-solving skills which will assist you to become an extended shot. Now you may recognise that why a majority of companies start their data science recruitment with problem-solving tests. You don’t need to be a master at it but a curious mind will assist you in forming this skill.
• Structured Thinking – the power to structure your thoughts and map each of them is certainly a must-have skill. Structured thinking is formed of use within the initial steps of the project where the matter statement and hypothesis are to be formulated.
• Storytelling Skills – A key skill that each one the info science and analytics professionals must have is that the ability to precise the info during a format that's understandable by the stakeholders – a story, it's this step that needs creativity and human skills.
Finally, allow us to delve into something you want to confine mind before starting your data science journey. Each one among us is exclusive and comes from different backgrounds. All the above points must be applied during a personalized manner to reap the utmost benefits.
The whole process might sound easy to implement during a linear fashion – learn Python -> machine learning -> deep learning then on, but that’s not the case during a real-world scenario and you would like that last piece of the puzzle to master data science – a mentor. At GICSEH, we make sure that you get one mentor and work on real-time projects.
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