Introduction to Key Data Science Concepts
What is Data Science?
In medieval times, there wasn't much data to be stored or processed, however; today, millions of data is generated every minute which needs quick processing and formatting. Data Science is the first strategy that comes into mind and therefore; it has vast importance in the IT sector. Data Science is the usage of cosmic techniques, tools, and algorithms to bring about essential information from the hidden patterns present in the raw data.
Data Scientists not only procure valuable information by exploring the hind sights of the unstructured data, but they also provide information regarding specific events which may have the chances to occur shortly. That is why Data Science is different from Statistics and how it can be used for the economic stability of the organizations.
Importance of Data Science
In this revolutionary phase of Information Technology, it is vital to utilize those frameworks that take minimum input and give out maximum output. One such framework is Data Science which provides immense benefits through seamlessness and effectiveness.
- Data Science helps in effective decision-making by providing the information explored from the raw data.
- Data Science uses some analytics that can help in making quintessential decisions about the products.
- Data Science helps in reducing the cost of incorporating heavy hardware and software on the premises of the organizations.
- Data Science helps in the production of smarter products by keeping in check the latest trends in the market.
- Data Science can help you to target audience depending upon the nature of the products that the companies are offering.
- Data Science helps in broadening the perspectives by constantly familiarizing with innovative and conventional techniques to look into the product's history.
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Key Data Science Concepts
Data Science may seem difficult to learn at first but if you have the motivation and appropriate concepts, nothing will stop you from making a promising and staggering career in Data Science. Below mentioned are some of the key concepts which you must understand and apply on your future projects.
- Probability and Statistics
Understanding and learning Statistics is the key to establishing a firm foundation of robust Data Science knowledge. All the other concepts will go in vain if you don't know the basic concepts of Statistics and the regarding distributions, estimators, and tests. You need to cover the following areas to build a strong base.
- Probability Distributions
- Bell Curve
- Experiment Analysis
- Bayesian Statistics
- Sampling Theory
- Confidence Intervals
- Dimensionality Reduction
- Tests of Significance
- The Theorem of Central Limit
- ETL and Preparation of Data
This is the most crucial concept of Data Science and to adhere to this concept, you must get familiar that all the data collected by the company comes in the unstructured and uncleansed form. After going through several Data Engineers, this unstructured data gets ready for the Data Scientists. However, sometimes, Data Scientists need to interpret this data on their own and it is called ETL of the data which is referred to as Extraction, Transformation, and Loading of the data. For this purpose, you need to find the following constituents:
- Preprocessing of the Data
- Verification of the Data
- Missing Data
- Data ETL Pipeline Automation
- Invalid Data
- Validation of the Data
- Programming Languages
Programming skills provide the basic information about the concerned set of the data which is then used to format it. The most commonly used programming languages are Python and R. Python is responsible for understanding Machine Learning, Visualization, Manipulation of the Data, and Vectored Computation. On the other hand, R is the programming language with is procreated by the Statisticians which can be easily understood if you know the basics of Statistics.
- Machine Learning
Machine Learning is utilized to solve more complex problems in the systems as the size of the data increases. It enhances the efficiency of the systems and provides prolific results that are used for better decision-making. Data Science is merged with Machine Learning and having the basic concepts of Machine Learning can excel you in Data Science. Machine Learning consists of different programming languages but the majority of it comprises Python and R. If you know about these two programming languages then Machine Learning is a piece of cake for you.
- Linear Algebra
Linear Algebra consists of all the matrices and vectors that are essential for interpreting information from the raw data. Every operation run by the organization starts from this basic area, thus; you must rewind and clear the concepts of linear algebra. You need to cover:
- Determinants
- Linear Systems of Equations
- Vector Algebra
- Matrix Operations
- Algebra Software that is aided by Computer
- Matrix Algebra
- Summary and Visualization of Data
The summary is giving the entire report on the life cycle of Data Science and how every system was set to draw out the concerned information. Visualization, on the other hand, consists of data coding and communicating visually using some of the conventional tools such as:
- Bokeh
- Matplotlib
- Tableau
- Seaborn
- ly
- ggplot2
- Google Data Studio
The Bottom Line
Grasping the above concepts on your own can result in looking for several books consisting of 10,000 pages and it's still not sure if you will acquire authentic and accurate knowledge. Therefore, we have brought you all-in-one Data Science Training that will enhance your already existing skills and help you to grasp all the concepts better with the help of several courses, assignments, projects, and much more.