Python is a general-purpose language which can be fine-tuned to serve data analysis purposes but is not limited to those only, like R and MATLAB. Also, it offers important advantages such as speed, performance, and scalability. An expert Python developer from Iflexion also mentioned flexibility and capacity, making this a great tool to handle Big Data projects. The extended community built around it adds to rating Python as a top choice, since you can always have someone to ask for help.

Where to Start

Python is so widespread it could be overwhelming to choose what course or framework to learn first. There are a lot of free and paid options, you just need to find a teaching style that speaks to you, no matter if you choose a more academic path or go for a gamified approach. Just make sure you understand the basics of syntax and logic. Don’t spend an overwhelming amount of time delving into all the nuts and bolts, especially if you just want to use it for data analysis. Get up to speed and then head out to learn the necessary libraries.

Python Basics

Since this is a programming language, you will need to get a good grasp of data types, functions, loops, string-specific operations and the use of third-party modules to make coding easier. You will also need to read from files and write to them and do some primary debugging to avoid propagating errors.

The way you interact with the Python environment is also your choice. If you prefer the Command Line (MS-DOS like), you can just use a text editor to write your code and run it, or you can use various interfaces like iPython and Jupyter notebooks if you prefer an interactive shell.

Data Analysis Libraries in Python

Like most programming languages, Python has its main functions grouped in libraries that can be accessed for functionality and which save developers from reinventing the wheel. Before writing any new piece of code it is worth learning about the pre-existing library bundles, especially for data science, which is well represented.

NumPy

The cornerstone of data analysis in Python, the Numerical library includes tools to work with arrays, including indexing, accessing multiple fields at once, changing the shape of an array, etc. The top feature of this package is that it offers vectorization of operations for increased performance and high speed.

Pandas

Since NumPy is optimized for numerical operations, it does not handle well other types of matrixes, such as tables and relational, which constitute an essential part of data analysis needs. For this matter, the Pandas are more appropriate tools since these include series (one-dimensional) and data frames (two-dimensional).

The Pandas include functions such as appending rows or columns to DataFrames, handling missing values and merging relational operations from SQL databases.

SciPy

If you’re looking for a specialized library useful for data science engineering and more, build on NumPy. It includes valuable tools such as math and physics constants, clustering algorithms, integration and interpolation functions, linear algebra routines and signal processing tools to name just a few of its capabilities.

The statistical functions module included in this package will constitute the base for classic data analysis, which can be enhanced by special functions or optimization algorithms.

Visual Libraries

Once you have computed your results, they need to be displayed in a visually appealing format, which can be done by calling the functions included in the basic Matplotlib package. The usual charts, histograms, and mappings can be created and customized up to a granular level.

This package helps Python rival efficiently with dedicated tools like Mathematica and MATLAB. This is not the only graphics library available for Python. Other examples include Seaborn based on Matplotlib for heatmaps, distributions and more statistic representations, and Bokeh which is independent and produces browser visualizations of data, much like an interactive dashboard.

Machine Learning and Big Data Analysis

The statistical analysis tools from the previous packages don’t cover the modeling part and econometrics, for which the Statsmodels library was created. Specifically for machine learning, there is the Scikit-learn package which has some of the same standard functions as Stats. This is not redundant, just a proof that these two disciplines evolved in parallel yet with different goals.

When it comes to Big Data, the rule of thumb is that if it doesn’t fit on your machine, it’s big and therefore needs parallelization. The best tool for this is PySpark, which is the next level in using Python and requires a considerable time investment to learn and master.

Text Analysis & Natural Language Processing (NLP)

A particular area of Big Data analysis is related to text analysis, sentiment analysis, and natural language processing, which are the core of AI’s ability to interact with humans in a way that resembles other people more than a machine.

There are quite a few libraries in Python to handle these tasks like NLTK, which offers functions to tag text, classify it, perform semantic reasoning or auto-summarization. Gensim is another open-source tool which can create word2vec or even document2vec parsing. Python also supports APIs for other libraries like the Java-based Stanford CoreNLP. 

What Are the Next Steps?

These are essential steps to help anyone interested in data science and analysis to get started using one of the friendliest and most powerful languages. The upside is that this approach requires no previous knowledge of Python, yet depending on the level of Python proficiency, the progress can be speeded up.

Not all of these steps are necessary, and probably not all of them need to be studied in depth. It all depends on the actual problem to be solved. Next, you need to become a creator yourself and combine the existing puzzle pieces into new functions, or even more complex modules.

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