1 Nov · 11 min read

You must have heard of Data Science at least once before this time. There is no doubt that it is one of those topics and areas which is one of the hottest and trendy business points. By leveraging the power of data science, you are the pilot for driving your core business’s success to its full potential. No matter if you are a data analyst or a business intelligence specialist. Or you play the role of marketers or financiers, you are always inclined to put your data skills a foot ahead. Data Science is an enormous field and one can get lost in its wide range of topics. Before dwelling deeper about Data science and its related topics to master, let us have some recapitulation about it.

Simply, without the existence of data, there cannot be any subsistence of Data Science. Data is considered as one of the most important resources of this modernist era of data explosion and technological advances. Major businesses and companies along with all tech giants are investing deliberately to obtain the best possible data resources. The only reason for this is to attain the desired client and customers’ satisfaction rate. With the collected data resources, the behavioural patterns are analysed to serve better to customers.

Data Science is the field of study that incorporates programming related skills along with domain expertise, and comprehension of mathematics and statistics to bring out meaningful perception of data. You can consider a real life example of applying algorithms to text, numbers, audio, images, videos to produce artificial intelligence(AI) systems to carry out tasks that generally require human intelligence. In turn, insights are being generated by these systems which you can translate into substantial business value.

There are various aspects of data science such as data preparation, data mining, data manipulation, data visualisations and other sorts of aspects linked to data. Data world is a vast field that is enclosed with mathematical and statistical topics for data science.

To look into the future scope, it is required to demonstrate rational reasons about why data science is crucial to modern day business needs. Let us have some focus on few factors that data science’s future points to:

Through any website transactions and interactions, businesses and companies collect data. Between this, those businesses and companies commonly face a challenge in categorising and analysing the data that is collected and saved. Such companies can level up their business productivity and progress a lot with efficient grasp of data with a data scientist.

There is a risk of stagnation when it comes to career scopes that do not fetch any growth prospects in them. Data Science is an extensive career path that is undergoing constant developments which promises plentiful opportunities in the future. Due to constant evolution of data science, employment roles are likely to get more definite which will lead to wide specialisations in the field.

With or without your notice, data is being generated by each and every one of us on a daily basis. The interaction you are having with data on a daily basis will only keep surging as time advances. Imagine about the amount of data that is being generated in the world with each passing day. As data production will be on the climb, the demand of data science will be on the ascent proportionally. And with this, data scientists’ demand will be on high rise.

We have picked up some basic and advanced topics to provide you an idea on the major concepts and concentrations about where to master your skills in the world of data science. Let us see few of such topics you need to quash:

- Programming
- Linear Algebra
- Statistics
- Machine Learning
- Data Mining
- Data Visualization

Programming is one of the most vital and essential topics that you need to pay attention to build data science projects. Remember that, if you are planning to pursue a career in data science, you will need to absorb how to code, and code well. It is a big advantage if you are having a computer science background like most of the other data scientists. You need not to be worried if you do not have such a background to be successful in the data science field. Like most of the things in life, programming can be self taught.

**When is it important and what should you know?**

It is an essential skill for data scientists to be able to program no matter which domain they are in. The power of programming really thrives into data science to automate tasks that save your valuable time while having your code much easier to understand, debug and maintain.

Also there are some of the crucial and practical skills that are related with programming in the field of data science. First one is the **software development practices** which are required on larger scale commercial projects. The next is to have experience of using databases. You should have preliminary knowledge on **SQL databases** and its commands along with its design practices. Last but not the least, to maximize productivity, you should have a good collaboration. Most of your data science work will be done in teams and groups. This is because ‘maintenance’ is an important aspect in data science and it is always better to collaborate and develop code which is easy to scale and maintain over time.

Linear algebra is that branch of mathematics which is correlated with vector spaces and linear mappings between such spaces. Simply put, it is dealing with straight stuff in space. In the field of Data science there is a huge significance of linear algebra. Matrices are the core concepts which are used on **tables and data frames**, the two fundamental data science structures. Below are the few main use cases for linear algebra:

**Modelling: **

In order to model data behaviours, you are likely to break down samples of data into subgroups to achieve accurate results and outcomes. This requires you to make use of matrix mathematics which includes inversion, derivation and much more.

According to Britannica, statistics is the science of collecting, analysing, presenting, and interpreting and extracting conclusions from a set of data. If you are an aspiring data scientist, you need to focus on statistical theory and practices. Let us see some of the significance of statistics in Data Science. The following practices are extremely considerable for interpreting data and getting productive results.

**1) Experimental Design and Pattern:**

Think of the situation where you are digging up to find an answer to some query or questions. You are most likely to administer some sort of experiment. Now link this with statistics in which you will be carrying on that experiment dealing with controlling data groups, sample data sizes and more.

**2) Recurrent statistics:**

Recurrent or frequent statistics allows you to calculate how much a data or result point matters by using different statistical practices such as hypothesis tests and confidence intervals. You can get knowledge to become a strong data scientist by being able to compute significance and various crucial pieces of information extracted from data.

**3) Modelling:**

At one point of your statistical journey in data science, you will be ending up using predictive modelling. If you are seeking to predict something related to data, find the reasoning behind a set of data, or to find a bigger picture of your data, there are multiple techniques which are often used for modelling work in data science. Few of those techniques are regression and clustering.

Generally, machine learning is when a computer is able to learn from experience without being specifically programmed. In other words, machines learn by themselves about how to program so that writing an explicit set of instructions for certain tasks gets lifted. It is a subset of artificial intelligence which is basically the science and engineering of building intelligent machines. Undoubtedly, it has quite a huge significance in the present day technology picture.

*“A Breakthrough In Machine Learning Would Be Worth Ten Microsofts!” — Bill Gates, Founder, Microsoft.*

ML is a fastest growing field and a crucial topic in the data science world. You must have heard of various applications of machine learning such as speech recognition, image classification, self-driving cars and many more. Machine learning algorithms make use of historical data as inputs to produce new values of output. Over time it has become more and more significant. Machine learning is classified into three forms of learning: supervised, unsupervised and reinforcement learning.

**1) Supervised Learning:**

When you’re feeding the ML algorithm with information to help it learn, it is known as a supervised learning technique. You will observe that practical machine learning is mostly done using this technique. You can think of this as a process in which an algorithm learns from data, producing the expected results and then you correct the results in order for the algorithm to refine in accuracy in the next run.

It becomes very useful in various business purposes such as inventory optimisation, sales forecasting, fraud detection, etc.

**2) Unsupervised Learning:**

Unsupervised learning is when an algorithm is not fed with any data and identifying as well as discovering the underlying structures in the data is typically done by the algorithm itself. This type of learning does not use sets of data, instead a machine looks for patterns in the data. It becomes handy when you need to identify patterns and to make decisions based on those patterns.

Unsupervised machine learning is widely being used to produce predictive data models. Common practical application is clustering. Based on specific association and properties having the rules existing between the data clusters, the process of clustering creates a model that groups objects together.

**3) Reinforcement Learning:**

This type of machine learning is quite similar to humans' ability to learn. The algorithm associated with it(reinforcement learning), learns by having interaction with its environment and getting a positive or negative based outcome or reward in ML terminology. For example, your bank loan eligibility can be determined by using a reinforcement machine learning algorithm. It may classify your profile as high-risk based on customer information it already has. In that case, the algorithm gets a positive reward and if your profile is at low-risk, it gives a negative reward.

You should be aware that it requires elevated computing power to have its full capability. This type of learning requires lesser management as compared to supervised learning. Practical examples for this type are still surfacing. It can have great use cases such as controlling traffic lights to minimize traffic jams or teaching vehicles to drive and park by themselves autonomously.

Data visualization is quite a self-explanatory topic. It is an act of conveying data and results through some sort of chart and picture. It is not always about how attractive a visualization looks. The aim is to communicate the insights that are found within the data in an easily understandable way. There are various types of data visualizations techniques available in data science.

**1) Multi- Dimensional**

Multi dimensional visualisation deals with charts and graphs having multiple variables. This is the most common type of visualisation that you will see. Examples of such visualisation include histograms, pie-charts and scatter plots.

**2) Geospatial**

Geospatial visualisations are related to geographical locations. These types are commonly being used to convey insights about a specific area and region. Examples of geospatial type of visualisation includes proportion symbol maps, dot distribution maps and contour maps.

**3) Time-Driven**

All time-driven visualisations make use of time as a baseline for conveying data effectively. For communicating any change happening over a period of time, this type of visualisation is of very powerful use. Example of time-driven visualisation includes gantt charts, times series, arc diagrams, etc.

In simple terms, it is the process of inspecting and exploring data sets in order to extract relevant and important information. To have visualisations, data scientists first need to invest time in collecting and preparing data. Data mining is a process of sorting a large set of data to identify relationships and patterns that can assist you in solving business issues through data analysis. To make more informed business decisions and to predict future trends, different organisations use different techniques as follows:

**1) Data Wrangling**

It is an act of transforming data from its raw form into a more feasible form. Data wrangling consists of a few crucial steps which includes parsing and cleaning into predefined structures.

**2) Data Cleaning**

Data Cleaning is a process in which correcting and detecting a set of data against inaccurate, corrupt and missing values. It is an important step in data mining which helps you extract proper visualization and processing.

**3) Data Scraping**

Data Scraping is involved in pulling information out of any website and putting it into a spreadsheet generally. This method is an efficient way to extract a good amount of information required for analysis, processing and presentation of data.

You might have realized by now how broad this data science field is, having a wide variety of distinctive sub-topics. Data Science has several aspects that are crucial which needs full focus and attention to master the subject. We have tried to include a few specific topics that hold wide significance. With the help of above mentioned concepts, you can achieve a wide range of your requirements and tasks and hence, it helps you to get the desired outcomes on your projects much faster. Don’t miss out on focusing on important aspects of programming languages like Python which is quite popular in the data science world. Also to get a solid foundation and good grasp of the core of data science, you must learn Databases/SQL and mathematics. To continue, you can read more on common mistakes in Data Science that you may make from here.

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