Python For Data Science: 5 Important Concepts You Should Know Today

Data science is a vast field that combines domain expertise, programming, statistics, and mathematics knowledge to create something meaningful. It is used by businesses in many different ways, and there's a massive demand for data scientists. 

Python is a leading language that is used in Data Science, and many Python developers are switching to the Data Science domain by learning some fundamental concepts of data science. Whether you are already a data scientist or you are looking to make a switch to data science by leveraging your Python skills, you'll need to have a good grip on five different concepts. 

Once you've mastered these five concepts, you can excel at data science jobs and be an outstanding data scientist.

To get started, we recommend Python with Dr. Johns, a course designed by our very own editor. It covers the fundamentals in a way only an expert could provide. By the end of the course, you'll understand the "why" behind Python code, so you can even write on a whiteboard (without the aid of autocorrect).

But before we explore those five concepts, it is essential to understand where and why businesses leverage data science in their processes. 

Where and Why Do Businesses Use Data Science?

Data science is magic for companies that know how to use it. There are many different applications of data science but most companies use data science for the below-stated use cases.

Improving Products and Services

Data science allows companies to understand their products and services better. It can be used to analyze and create models that understand user reviews so that insight can be taken from them for improvement. 

Data science helps businesses to run A/B testing experiments on a wider audience base and interpret the results. These experiments can be run indefinitely, and companies can find out areas where they can improve their products and services. 

Mitigate Risks

Data science helps businesses predict their revenues, sales, and other vital factors. Using those predictions, companies can make better decisions that minimize their risks. It can also help to simulate risk situations so that management can keep practicing their risk-averting capabilities. Such simulations enable companies to find flaws in their risk-mitigating policies and come up with more potent and robust policies. 

Providing Better User Experience

Providing a better user experience is quite essential to retain customers and growing your business as 33% of users are ready to change companies just after their first bad experience. Without good research, there is significantly less chance that you can improve user experience. This is where data science helps companies. It allows them to experiment with different things and collect metrics on them so that they can be analyzed later to make product decisions. 

Iterating this every now and then helps companies to polish their product’s user experience. 

Gain Competitive Edge

Data science is not used by all companies. For some, that's a major oversight. For others, it's a sign that the company doesn't yet collect enough information.

To use data science, companies need to have lots of historical data and operational data for reference. If both of these are present, they can leverage data science and gain a competitive edge over other businesses in the segment. 

As we saw above, data science allows companies to make better decisions, mitigate risks, and find out their growth options by making the user experience better. They need a lot of different skills from their data scientists. But in the end, you can group those skills and concepts into just five buckets. 

Going forward, we will have a look at the five concepts you should know today to become a better data scientist while using Python. 

5 Concepts You Should Know Today

1. Statistics

Statistics is the base of data science. Everything you do in data science will have some sort of statistics involved in it. The results of any data science project depend heavily on your knowledge of statistics, and to become a good data scientist, you should indeed have excellent statistics knowledge. 

The community at Hackr curated a list of our favorite data science courses, and many start with statistics. It's often one of the first courses you'll take in the data science specialization. For example, this Mathematical Biostatistics Boot Camp from Johns Hopkins University is taught by Dr. Brian Caffo.

Certain concepts have high weightage, and it is best to learn and understand them well, as you’ll be applying those concepts quite regularly. Some of those concepts are: 

Probability

Probability shows the possibility of the occurrence of an event. Real-world is full of events, and when you are creating data science models, you'll rely heavily on probability. Probability distributions help you understand and answer business questions based on the underlying datasets. Hence a thorough knowledge of this is quite essential.

This topic goes hand-in-hand with statistics. Students who want to excel in data science will also need to understand probability and statistical significance in order to make value judgments on large data sets.

Hypothesis Testing

Data science involves making assumptions, storytelling, and backing those assumptions with the available data. Hypothesis testing is an important statistics concept that allows you to validate your hypothesis based on the data you have. First, you create a hypothesis after exploring the data, and then you try to support your hypothesis by backing it with data with the hypothesis testing method. 

If you want to present your findings confidently and push them to a broader audience, you'll need to do a lot of hypothesis testing in your projects. 

Regression

Every dataset has relations hidden inside it. While data mining and exploratory data analysis help you uncover those relationships, regression helps you to understand the relationships between variables. 

You can use regression to understand the relationship between more than two variables and features of your dataset. As a data scientist, you'll be using regression analysis and regression models on a regular basis to predict and forecast things for your business. 

2. Programming

As much as statistics is important, knowledge of programming is equally important. To create models, load datasets, model data, and perform many other operations, you need knowledge of programming languages. The knowledge of programming can be broken down into three different segments when it comes to programming for data science.

In-depth Knowledge of Python

The first and foremost step of programming for data science with Python is to have in-depth knowledge of Python. You can acquire this knowledge by going through the official documentation, tutorials, articles, and paid courses. Start off with the basics of Python, and advance your learning journey to OOPs and other such concepts. 

Having in-depth knowledge of Python will make it easy for you to start learning different things in data science and implement machine learning algorithms. 

Dr. Johns covers this in his Python Starter Kit, which gets students started with installing Python, before diving into the more robust course.

Algorithmic Thinking and Data Structures 

Algorithmic thinking and data structures knowledge is quite essential. It helps you understand and solve problems effectively. Algorithmic thinking allows you to break down problems into smaller versions and solve them in pieces collectively to solve your data science problem statements. 

Knowledge of data structures helps you load data and handle them efficiently so that primary storage is always available for other tasks. 

We compiled a curated list of courses and tutorials on data structures. The community shares their favorites and discusses their experiences to share which offers the best value.

Understanding of Libraries

There are tons of libraries already available to implement a data science lifecycle. There is no need to create everything from scratch. Python has extensive library support, and there are libraries available for machine learning models, data preprocessing, data handling, visualization, and many other things. You can save significant time and effort by understanding libraries.

3. Databases

Data is the primary ingredient in any data science project. As a data scientist, you'll spend significant time storing and retrieving data from databases. Most data is stored in relational databases, and you need to have an understanding of those. 

Many times you'll participate in the creation of databases for products so that analytical tasks are accessible when the time comes. Hence you should have enough theoretical knowledge and implementation knowledge of database management systems. 

You should understand different database concepts like indexing, SQL querying, modifying stored data, running analytical workloads to get desired answers from data, etc. Moreover, you should have knowledge of maintaining database management systems with high security at different enterprise levels. 

Knowledge of different databases and SQL, in general, will help you explore all sorts of data effectively.

To get started here, we compiled the best SQL courses for beginners and more advanced programmers.

4. Big Data

Every day we generate thousands of GBs of data on the web. Users of the internet are increasing exponentially, and they contribute to the increasing data every day. This renders the older data analysis and data science techniques ineffective. Data engineering is used to store and manage big data, and as a data scientist, you have to embrace this technology too. 

You may not require in-depth knowledge of building data warehouses and some other specific technologies, but having general knowledge is enough. You need to understand the Hadoop infrastructure, distributed processing, cloud computing, and allied big data technologies to run data workloads effectively. 

Moreover, knowledge of big data technologies also helps you in creating better data modeling pipelines as you can know the insides of data processing and warehouse capabilities. 

5. AI & Machine Learning

Data science has a part where you might create models. These models are machine learning models that can help you in varied tasks like prediction, classification, clustering, etc. While you’ll mostly work on these three kinds of tasks, it is important to have significant knowledge of machine learning and artificial intelligence. 

Data science jobs have a broad scope, and you never know what kind of role you are asked to perform. Hence, having knowledge of different algorithms like linear regression, linear classification, decision trees, random forests, k-nearest neighbors, k-means clustering, etc., will help you excel at your role. 

While you will spend a lot of time in feature engineering and data cleaning, you'll build models at the end based on your business requirements. 

Data science is a research job role, and you'll always be experimenting with different things to come up with solutions. In most cases, data science problem statements are very vague, and a lot depends on your skills and domain understanding to come up with solutions. If you have enough knowledge about these 5 concept groups, no one can stop you from becoming an excellent data scientist. Even if you don’t have one, you can hire Python developers who know these concepts. 

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