Data science is amongst the fastest growing technologies today because of the growing data amount. Data scientists are currently at an advantageous position with relation to employment and earnings due to this rise. This further sprouts important questions like how to learn data science and even why learn it. This article is a complete guide covering all aspects around its learning.
The estimated global data volume in 2025 is 175 zettabytes. This number clearly reflects the need for data scientists, analysts, engineers and other professionals in the field. It is thought to be a leading skill today because the need and demand for data science professionals is also on an upward trajectory. But why should one learn data science? The next section outlines an answer for the same.
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One should have a solid reason to learn data science. It is definitely a growing field with many career and future possibilities and options. Knowing the why behind stepping into something is very important for complete dedication towards learning it. Here are five reasons to pump up a learner's enthusiast in this direction.
The demand for data science professionals has taken the steeper route and is still on it. High demand is equal to high earning potential and also amongst the biggest attractions of any profile. The average annual salary of a data scientist in the US is around $117.90k. This salary number falls around INR 14.50 LPA in India.
There are different factors that affect the final package a person gets. Geographical location of the person and company, certifications, experience, skill set, and academic achievements are some of the common factors that make it to the top. These professionals can negotiate a higher package through their skill set and knowledge base.
Data science experts incline towards showing off a broad skill set. This field is one where the person becomes a lifelong learner and that also reflects in their job role and salary package. They have great analytical skills, pay attention to details, have problem solving abilities, show organizational skills and are innovative too. They are great communicators to work with different professionals and teams.
Data science professionals do not have just one option of becoming a data scientist. It is in fact a title that comes much later after gaining good experience and expertise. Other common employment opportunities that aspirants prefer are -
Companies need data like human beings need water. A company's growth is highly dependent on the way they use this data and that means getting professionals on board. Data scientists, engineers, analysts, statisticians, ML engineers and business analysts are a few professionals whose career revolves around this demand. Many industries like education, medical, technology, entertainment and science are making the most of this technology. Even governmental organizations are adopting it at a good rate.
It is changing as other related technologies are changing. Many new tools, techniques and applications are coming forth that are making better with time. Passionate individuals get continuous learning possibilities and continuous upskilling.
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This section covers the main question of the hour and that is how to learn data science. This learning journey is different for different people because neither their goal is exactly the same nor is their technical knowledge. But there are still certain steps that fall under the must-know category.
Math is an important skill for a strong theoretical foundation in this field. Statistics and probability are two other very important skills to have because they build models and algorithms. Bayes theorem, correlations, variance and conditional probabilities are a few concepts to begin with.
Professionals in this field need a strong base in working with in-demand programming languages. Python and R rank highest on the list and are good places to begin. They are free and open source, and thus best for aspirants. Both are beginner friendly with plenty of great libraries and a simple syntax.
These two languages can accomplish almost all tasks in this interdisciplinary field. They are very strong in different aspects and areas. Python makes web scraping, workflow automation and deep learning tasks a breeze. R is better when translating statistical approaches towards computer models.
Database knowledge means having skills in data retrieving and storing it after processing. SQL is the preferred database choice for such professionals everywhere but there are many other great ones too in the market now. PostgreSQL, MongoDB, Microsoft SQL Server, Oracle database, Snowflake and Amazon Redshift are a few names.
Data analysis includes different methods, tools and techniques. There are many different approaches and which one the data scientist picks depends upon their goal and intended result. Some common data analysis methods are -
There is no dearth of amazing and useful data science tools today. One can pick advanced tools like SAS, MATLAB, BigML and Tableau or simpler ones like Microsoft Excel. Different tools have different uses and not at the same time or circumstance. This means that the professional is in charge of deciding which one they want to pick according to the project.
Completely understanding the right way to learn data science is only possible with an online training program. This is more like an opportunity to explore another expert's accumulated knowledge and skill. Their expertise will mold the learners skill set and knowledge base due to close learning.
Self paced learning is another helpful option these days. The aspirant can get started in any field irrespective of how hectic or packed their schedule is. They can learn at their own pace and in their own time.
There are plenty of applications of data science today. This number is multiplying at a very rapid rate because of its integration with different technologies like artificial intelligence, machine learning and deep learning. AI developers find data science a powerful tool for training and developing deep learning models. Its use has spread to some of the biggest industries in the globe.
There are many examples of this field in education for both educators and learners. Teachers get a better look at a student's performance and areas of development or additional support. Schools can better analyze a teacher's performance or lack thereof. It also improves curriculum by adjusting it according to the learner's capability.
Data science tools simplify data analysis from transportation methods, supply chain aspects, freight types and others. It optimizes shipping routes according to weather conditions and other natural incidents. This saves time and money.
Amazon and Netflix are two of the biggest users of this interdisciplinary field for their recommendation systems. Data science analyzes user behavior on different factors like clicks, searches, watches, saves and buying patterns. It understands the attraction points for a customer and suggests them in line with it.
Many industries find this field helpful for detecting frauds and risks. Computer network security professionals look for suspicious patterns or activities that might be an indication of security threats. Cyber security professionals can even retrace a cybercriminal's steps, find clues and alert others about related threats.
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There is no end to how much a person can read about ways and tricks to learn data science. It is a growing field and that means its learning pattern keeps changing too. There are a few inconsistencies like the skills and tools one needs to begin with. This article has covered the basic steps to begin and then the journey is a long one that will span over a long period of time.
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Online is probably the best way to learn data science. Start with an online course to get certified. One can also go through free sources like articles, tutorials and YouTube videos. There are endless things to learn from.
SQL is amongst the must-have skills for this field.
Three to four months is enough to get ahead with the foundational skills for this field. Excelling at it is a lengthy task that needs continuous learning.
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