Data Analysis with Python Part 4 of 6 Analyzing tabular data with Pandas

How do I start learning Python for Data Analysis? Check it out on our courses portal and start your data science journey today. Step 0: Warming up. ... Step 2: Learn the basics of Python language. ... Step 3: Learn Regular Expressions in Python. ... Step 4: Learn Scientific libraries in Python – NumPy, SciPy, Matplotlib and Pandas. ... Step 5: Effective Data Visualization. How do I learn data analytics in Python? How to Learn Python for Data Science the Right Way Learn just the basics of Python. ... Numpy and Pandas - An Excellent resource to learn them. ... Learn to visualize data using Matplotlib. ... How to use SQL and Python. ... Learn basic Statistics with Python. ... Perform Machine Learning using Scikit-Learn. ... Conclusion. What is the best way to learn data analysis for a beginner? No More Excuses: 10 Best Ways to Learn Analytics Online edX Data Analysis & Statistics Courses. ... National Tsing Hua University's Business Analytics Using Forecasting via FutureLearn. ... Codecademy's Learn SQL. ... Big Data University's Analytics, Big Data, and Data Science Courses. Is Python good for data analysis? As we have mentioned, Python works well on every stage of data analysis. It is the Python libraries that were designed for data science that are so helpful. Data mining, data processing, and modeling along with data visualization are the 3 most popular ways of how Python is being used for data analysis Looking for top Python Developers? They are just a few clicks away. Services Features Case studies Blog Contact Join as a Talent All Software Design Project Management Business & Startups Why Is Python a Great Choice For Data Analysis? Apr 19, 20215 min read Dawid Karczewski Senior full stack developer and CTO at Ideamotive. According to a forecast from International Data Corporation, the worldwide revenues of Big Data and Business Analytics solutions would reach $260 billion by the end of 2020. This is no wonder, as data analytics helps businesses predict customer needs, personalize their approach to customers, prevent failures and make better business decisions. Consequently, the popularity of data analytics is constantly growing. If back in 2015 only 17% of companies have been utilizing big data analytics, in 2017 the percentage has grown to 53% and is getting higher each year. In order to join the top companies that use data and benefit greatly from it, you have to know at least one programming language used for data science. In this article, we will take a look at one of these most widely-used data science programming languages – Python. Find out whether Python is good for data analysis, how to use Python for data analysis, its pros, and cons, and what alternatives there are for data analytics. Python_ The Definitive Business Guide Is Python Good For Data Analysis? Python was introduced back in 1990 but it began to gain popularity only a couple of years ago. In 2020, Python became the fourth most used language after JavaScript, HTML/CSS, and SQL, with 44,1% of developers using it. Python is an interpreted, general-purpose, high-level language with an object-oriented approach. The language is used for API development, Artificial Intelligence, web development, Internet of Things, etc. The part of why Python has become so popular is because it is widely used among data scientists. It is one of the easiest languages to learn and has impressive libraries and works perfectly for every stage of data science. So the short answer to the question of whether Python is good for data analysis is yes. We will discuss its pros and cons later in the article so stick around to find a more detailed explanation to the question. How is Python Used For Data Analysis? As we have mentioned, Python works well on every stage of data analysis. It is the Python libraries that were designed for data science that are so helpful. Data mining, data processing, and modeling along with data visualization are the 3 most popular ways of how Python is being used for data analysis. Data Mining A data engineer uses libraries such as Scrapy and BeautifulSoup for data mining Python-based approach. With the help of Scrapy, one can build special programs that can collect structured data from the web. It is also widely used for collecting data from APIs. BeautifulSoup is used when one can not retrieve data from APIs: it scrapes data and arranges in the preferable format.