Is Data Science Hard?

So, you are confused about learning Data Science and wondering if it is hard to learn!!

There are many factors that you must take into consideration while deciding to move into any career. Looking forward to taking a data science course in Delhi with placement also requires you to have some strong reasons.

Data Science: Quick Facts

Almost 20% of the overall technology budget is invested in data analytics by 73% of companies
Almost 70% of online data is created by individuals, of which 80% is stored, maintained, and analyzed by enterprises
Facebook’s Open Graph API aggregates a whopping 1 billion pieces of content every day
It is predicted that, by 2025, more than 75 billion IoT devices will be in use which is three times more than the IoT-enabled devices in 2019
Due to poor quality of data, organizations suffer a financial loss of almost 15 million dollars in a year
For a Fortune 1000 enterprise, it is possible to get additional revenue of USD 65 million by just a 10% increase in the accessibility of data
The fact that data analytics has significantly and fundamentally transformed the way industries compete is believed by almost 47% of organizations
Nearly 62% of retail companies have gained a competitive advantage by adopting data analytics techniques

Ain’t these powerful stats?

So much that you wish to know more about making a career in Data Science, isn’t it?

Let’s read ahead to know more about Data Science and how you can make your career in this domain.

What is Data Science?

Data Science, according to Wikipedia, ‘is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.’

It is the domain of study that is concerned with huge volumes of data making use of modern tools and techniques to identify patterns, extract meaningful information to support decision-making.

Complex machine learning algorithms are used in data science to build predictive models. The data may come from many different resources and can be structured, unstructured, or semi-structured.

The typical workflow in data science includes a series of processes that may include data acquisition, data cleansing, data warehousing, data processing, data clustering, data staging, data modeling, and summarizing insights.

After obtaining the insights, as a data scientist, you have to explore the data and insights, perform regression, text mining, qualitative analysis, and predictive analysis. Then, these insights are conveyed through techniques of data visualization to help executives in making informed decisions.

The Data Science Lifecycle

The five stages in the Data Science LifeCycle are:

Collect: this involves steps such as data entry, data acquisition, data extraction, and signal reception and requires you to gather raw data which may be structured or unstructured.

Maintain: the processes include data cleansing, data warehousing, data processing, data staging, data architecture. This stage requires you to take raw data and transform it into a user-readable format.

Process: Data Modeling, Data Mining, Clustering or Classification, and Data Summarization. For this step, you may take prepared data and explore its ranges, patterns, and biases to identify the ways it can be used for predictive analysis.

Analyze: Confirmatory/Exploratory, Text Mining, Regression, Qualitative Analysis, and these are the most crucial steps in data science. It is this stage that requires you to perform different analyses over the data.

Communicate: Data Visualization, Data Reporting, Decision Making, and Business Intelligence. This final step requires you to prepare the analyses in a user-readable format that may be in the form of graphs, charts, or reports.

Technical Prerequisites for Data Science

Some of the crucial technical concepts that you must master before initiating learning Data Science Master program in San Francisco are:

Machine Learning and Artificial Intelligence

The backbone of Data Science is Machine Learning. You should have sound knowledge of ML algorithms as they help in analyzing huge data sets and also automate iterative tasks.

Some of the algorithms you should know well are neural networks, adversarial learning, Time Series, reinforcement learning, etc.

Modeling

Mathematical models help you make calculations quickly and make predictions on what you know about the data. It is also a part of machine learning and helps you identify which ML algorithm is best suited to solve the given problem.

SQL Databases

According to a list of top skills released by LinkedIn, SQL topped the list. Proficiency in SQL is the most important prerequisite for all Data Science job roles. It is the programming language that is used to query data held in relational database management systems.

Statistics

Where there is data science, there are statistics. It is at the core of data science. Expertise in concepts of statistics enables you to extract more intelligence and acquire more meaningful results.

Programming

To execute a data science project successfully you need to have basic knowledge of programming languages. If you have expertise in Python, it can be really rewarding. It is a versatile language and is used by most data scientists for all of their tasks. Some of the Python packages that you should know well are Pandas, NumPy, Matplotlib, Scikit-learn, PyTorch, and Seaborn.

R; programming is another language that is widely used in data science, and expertise in this language can also be beneficial.

Educational Requirements

A bachelor’s degree in Information technology, Computer Science, Mathematics, Engineering, Statistics, and any related field can enable you to enter the field of Data Science easily.

Conclusion

What did you conclude while going through the lines of this article?

Is it really difficult to learn Data Science?

Seeing the prerequisites may make you feel that it is difficult. But it is not really difficult. Yet you can take up an online training course and get certified. These training courses have kept everything in place for you. Furthermore, you can learn at your own pace, and that too with top-class learning management systems. It makes you go through real-life projects and the training is conducted by industry experts.

Enroll Yourself Now!!