Do you know? Forbes says that they perform 40,000 search queries every second (on Google platform alone), which makes it 3.5 billion searches per day and 1.2 trillion searches per year.”
And it becomes considerably complex to deal with all these huge numbers and equations if you are not a Data scientist.
So, here comes the question-
WHAT IS DATA SCIENCE?
As the world entered the era of vast data, the need for keeping them safe and having a proper knowledge on how-to also grew. Data Science is a blend of algorithms, tools and machine learning methods with the goal line to discover hidden patterns from the raw data.
A Data Analyst usually explains what is going on by processing the historical data.
So, Data Science is mainly used to make decisions and predictions using predictive causal analytics, prescriptive analytics (predictive + decision science) and machine learning.
WHY DO WE NEED DATA SCIENCE?
Data is generated from various sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this enormous volume and variety of data. This is why we need more efficient, complex and advanced analytical tools and algorithms for processing, analyzing and bringing out meaningful insights out of it.
With the help of customer’s past browsing history, purchase history, age and income, data science recommends products with more precision
Even weather forecasting, detecting natural calamities can be done with the help of data science
Now let’s take an example to understand the role of data science in decision making – What if your car had an intelligence to drive you to your office? Self-driving cars collect live data from sensors, including radars, cameras and lasers to lay out a map of your surroundings. Based on this data, making use of advanced machine learning algorithms, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn, where to park, etc.
Business Intelligence (BI) Vs DATA SCIENCE
BI basically analyzes the past data to find hindsight and insight to describe the market trends. BI allows you to take data from sources, prepare it, run queries and create dashboards to answer the questions like quarterly revenue analysis or business queries.
Data Science is a more forward-looking approach, an exploratory way with the focus on analyzing the past or present data and predicting the future upshots with the aim of making informed decisions.
Data science in the 21st century has become an important aspect that it applies in almost all the spheres of any work. Given below is the info graphics which shows all the domains where data science plays a major role:
WHAT ARE THE SKILLS REQUIRED FOR LEARNING DATA SCIENCE?
A data scientist is a better statistician than any software engineer and that also, a better engineer as compared to any statistician.
To perform work effectively, there must be some technical and non-technical skills to acquire by those whose interest lies in data science.
The Non-technical skills are as follows:
1. Data Inquisitiveness
Curiosity or Hunger to learn more is the key towards acquiring mastery of any quantitative field. Since Data Science is highly quantitative in nature, it requires an expert who is armed with curiosity.
Since Data Science is constantly evolving, you must stay ahead of the arc by updating yourself with articles, blogs, new updates in programming languages,etc, just like you are doing right now. It shows that you have a high magnitude of intellectual curiosity for learning new concepts and executing them.
2. Business Acumen
Data Science revolves around the domain of business and therefore requires the data scientist to have a sound knowledge of the business requirements. The main objective of a data scientist is to translate business problems into data science solutions through the implementation of analytical skills.
3. Communication Skills
Communication Skills are extremely important for Data Scientists. It is one of the non-technical skills that you can never ignore. Some of the important areas in Data Science where communication skills are important are Data Visualization and Storytelling.
The Technical skills are as follows:
1. Python Coding
Python is one of the most common coding languages which is required in data science roles, along with Java, Perl, or C/C++. Python is a significant programming language for data scientists. This is why 40% of respondents when surveyed use Python as their major programming language.
2. Hadoop Platform
Although this isn’t always a requirement, it is highly preferred in many cases. Familiarity with cloud tools such as Amazon S3 can also be advantageous.One can encounter situations where the volume of data they have exceeds the memory of the system, this is where Hadoop comes in.
Note: A study denotes that 3490 LinkedIn jobs ranked Apache Hadoop as the second most important skill.
3. SQL Database/Coding
Even though NoSQL and Hadoop have become a great component of data science, it is still expected that an applicant will be able to write and implement complex queries in SQL.This is because SQL is precisely designed to help you access, communicate and work on data. It gives you accurate insights when you use it to query a database.
4. Apache Spark
Apache Spark is becoming the most prevalent big data technology worldwide. The only difference is that Apache Spark is faster than Hadoop. This is mainly because Hadoop reads and writes to disk, which makes it a bit slower, but Spark caches its computations in memory. Spark makes it likely for data scientists to prevent loss of data in data science
5. Machine Learning
If you’re at a big company with huge amounts of data, or employed at a company where the product itself is exclusively data-driven (e.g. Netflix, Google Maps, Zomato, Uber), it may be the case that you’ll want to be familiar with machine learning methods. If you really wish to stand out from other data scientists, you need to know Machine learning techniques.
6. Data Visualization and Communication
‘A picture is worth a hundred words.’
Visualizing and communicating the idea behind thedata is extremely important. It is fairly necessary for new companies that are making data-driven decisions for the first time. When it comes to communicating, this means describing what you’ve found. What really matters is how one implements his idea!
7. Data Wrangling
Often, the data you’re analyzing is going to be messy and tough to work with. Because of this, it’s really important to know how to deal with imperfections in data and somehow make it perfect. Few examples of data imperfections include missing values, inconsistent string formatting, etc.
8. Statistics and other concepts
A good understanding of statistics and a penchant with numbers is vital as a data scientist. Statistics is important at all company types. Also, understanding Linear Algebra concepts and Multivariable Calculus is very important at companies where the product is defined by the data, and small improvements can lead to huge wins for the company in predictive algorithm optimization.
These were some skills needed to become a great data scientist. After all, Data Science is a money-spinning career that draws a lot of people and thus, requires a lot of investment when it comes to skills.
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