Python for data science 101
Tags: datascience, python, beginners
Published: 2023-02-19 20:02:23

The interdisciplinary field of data science involves the extraction of knowledge, insights, and useful information from data. To analyze and interpret data, it encompasses multiple elements of statistics, mathematics, computer science, and specialized knowledge.
To put it simply, data science entails gathering, filtering, and processing data before utilizing statistical and computational techniques to derive conclusions and make predictions. These discoveries can be applied to making of business decisions, the creation of new products, or the recognition of patterns in huge databases.
The importance of data science has increased significantly in recent years as businesses from a wide range of industries use data to improve decision-making and increase their competitive advantage. Statistics, data visualization, machine learning, programming, and other techniques are frequently included in a data scientist's skill set.

_About Python_

Python is a high-level, interpreted programming language that is designed to be easy to learn, read and write. It is open-source and has a large and active community of developers who contribute to its development and maintenance. Python is widely used for a variety of applications such as web development, scientific computing, data analysis, machine learning, and more. 
Python is well-known for its readability and simplicity. When compared to other programming languages, Python code is simpler to understand and may be written in less lines. As a result, newcomers who are just entering the world of programming find it to be the perfect language.
Additionally, Python comes with a sizable number of modules and frameworks that make it simple to complete sophisticated tasks. For data analysis and visualization, some libraries are NumPy, Pandas, and Matplotlib; for web development, Django and Flask; and for machine learning, TensorFlow and Keras.
The cross-platform compatibility of Python is another key characteristic. Many operating systems, including Windows, Linux, and macOS, can run Python code. This implies that programmers don't need to make substantial changes to their code in order to deploy it once across several platforms.
Python is renowned for having a vibrant community. The Python community is dynamic and thriving, and many programmers contribute to its creation and support. Python has a wealth of internet resources at your disposal, including as tutorials, forums, and documentation. Beginners will find it simple to learn Python and to seek assistance when they do.
Last but not least, Python is a very adaptable language that may be used for a variety of purposes. It is employed in a wide range of sectors, such as finance, healthcare, aerospace, and more. Anyone wishing to get into programming will find it to be a useful skill due to its popularity and adaptability.

**_Python libraries for data science_**
_NumPy_:
 NumPy stands for Numerical Python. This library serves as the basis for all other Python data science libraries. Using NumPy, multidimensional arrays and matrices may be operated on quickly and effectively. Moreover, it offers a variety of mathematical operations for the study and modification of data. In scientific computing and research, NumPy is frequently utilized.
Some of the numpy methods include:
1. numpy.zeros(): This method creates an array of zeros with the specified shape and data type.
2. numpy.ones(): This method creates an array of ones with the specified shape and data type.
3. numpy.random.rand(): This method generates an array of random numbers from a uniform distribution between 0 and 1.
4. numpy.random.randint(): This method generates an array of random integers within a specified range.
5. numpy.linspace(): This method generates an array of equally spaced numbers within a specified range.
6. numpy.reshape(): This method changes the shape of an array to the specified shape.
7. numpy.concatenate(): This method concatenates two or more arrays together.
8. numpy.mean(): This method calculates the mean of the values in an array.
9. numpy.std(): This method calculates the standard deviation of the values in an array.
10. numpy.max(): This method finds the maximum value in an array.
11. numpy.min(): This method finds the minimum value in an array.
12. numpy.transpose(): This method transposes an array.
13. numpy.dot(): This method performs dot product of two arrays.
14. numpy.vstack(): This method stacks arrays vertically.
15. numpy.hstack(): This method stacks arrays horizontally.
...and many more!

_Pandas:_
Python has a robust data manipulation module called Pandas. It offers data structures for working with structured data, including SQL tables, Excel spreadsheets, and CSV files. Pandas offers a variety of tools for preprocessing, cleaning, and analyzing data. Moreover, it supports working with missing data and time series data.
Some of methods used from this library are as follows:
1. head() - This method is used to view the top 5 rows of a DataFrame.
2. tail() - This method is used to view the last 5 rows of a DataFrame.
3. describe() - This method is used to get the statistical summary of a DataFrame.
4. info() - This method is used to get the concise summary of a DataFrame.
5. value_counts() - This method is used to get the count of unique values in a Series.
6. sort_values() - This method is used to sort a DataFrame by values along a specified axis.
7. drop() - This method is used to drop specified labels from rows or columns of a DataFrame.
8. groupby() - This method is used to group a DataFrame by one or more columns.
9. apply() - This method is used to apply a function to each element of a DataFrame.
10. pivot_table() - This method is used to create a pivot table from a DataFrame.
11. merge() - This method is used to merge two or more DataFrames on a specified column.
12. join() - This method is used to join two or more DataFrames on a specified column.
13. set_index() - This method is used to set a column as the index of a DataFrame.
14. reset_index() - This method is used to reset the index of a DataFrame.
15. fillna() - This method is used to fill missing values in a DataFrame with a specified value or method.

_Matplotlib:_
Python has a module called Matplotlib that is used for displaying data. It offers a number of tools for making histograms, bar charts, line plots, scatter plots, and more. Matplotlib offers a variety of options for altering the appearance of visualizations and is very customisable. In data analysis and scientific research, Matplotlib is frequently used.
Some of the methods are:
1. plot() - This method is used to create a line plot.
2. scatter() - This method is used to create a scatter plot.
3. bar() - This method is used to create a bar chart.
4. hist() - This method is used to create a histogram.
5. pie() - This method is used to create a pie chart.
6. imshow() - This method is used to display an image.
7. subplots() - This method is used to create multiple plots in one figure.
8. legend() - This method is used to add a legend to a plot.
9. title() - This method is used to add a title to a plot.
10. xlabel() - This method is used to add a label to the x-axis of a plot.
11. ylabel() - This method is used to add a label to the y-axis of a plot.
12. xlim() - This method is used to set the limits of the x-axis.
13. ylim() - This method is used to set the limits of the y-axis.
14. grid() - This method is used to add grid lines to a plot.
15. savefig() - This method is used to save a plot as an image file.
...and many more!

_Seaborn:_
Seaborn is a Python library for data visualization. It offers a variety of high-level tools for producing statistical visualizations and is developed on top of Matplotlib. For making intricate visualizations like heatmaps, cluster plots, and violin plots, Seaborn is extremely helpful. Also, it offers a variety of choices for altering the appearance of visualizations and is very customizable.
The following seaborn methods are used within the context of a dataset:
1. sns.set(style="whitegrid"): This method sets the style of the seaborn plots to a white background with grid lines.
2. sns.barplot(x="class", y="survived", hue="sex", data=titanic): This method creates a bar plot showing the survival rate of passengers on the Titanic, with bars separated by class and color-coded by sex.
3. sns.countplot(x="class", hue="who", data=titanic): This method creates a count plot showing the number of passengers in each class of the Titanic, separated by gender.
4. sns.distplot(df["column"], kde=False): This method creates a histogram of a given column in a data frame, with an optional kernel density estimation.
5. sns.boxplot(x="day", y="total_bill", data=tips): This method creates a box plot showing the distribution of total bills for each day of the week.
6. sns.pairplot(iris): This method creates a pair plot showing the relationships between each variable in the iris dataset.
7. sns.jointplot(x="petal_length", y="petal_width", data=iris): This method creates a scatter plot with a marginal distribution for two variables in the iris dataset.
8. sns.lmplot(x="petal_length", y="petal_width", data=iris): This method creates a scatter plot with a linear regression line for two variables in the iris dataset.
9. sns.heatmap(corr): This method creates a heatmap showing the correlation between each variable in a correlation matrix.
10. sns.clustermap(corr): This method creates a dendrogram showing the clustering of variables in a correlation matrix.
11. sns.catplot(x="day", y="total_bill", hue="sex", kind="swarm", data=tips): This method creates a categorical plot with swarm points for the total bills of each day of the week, separated by gender.
12. sns.lineplot(x="year", y="passengers", data=flights): This method creates a line plot showing the number of passengers over time in the flights dataset.
13. sns.relplot(x="sepal_length", y="petal_length", hue="species", data=iris): This method creates a scatter plot with a different color for each species in the iris dataset.
14. sns.violinplot(x="day", y="total_bill", hue="sex", data=tips): This method creates a violin plot showing the distribution of total bills for each day of the week, separated by gender.
15. sns.stripplot(x="day", y="total_bill", hue="sex", data=tips): This method creates a strip plot showing the distribution of total bills for each day of the week, separated by gender.
...and many more!

_Scikit-learn:_
Scikit-learn is a Python library for machine learning. It offers a variety of classification, regression, clustering, and other techniques. Moreover, Scikit-learn offers tools for feature selection, data preprocessing, and model validation. For creating machine learning models, both industry and research use it extensively.

_TensorFlow:_
TensorFlow is a Python library for machine learning. A variety of tools are offered for creating and refining deep learning models. Building neural networks for the categorization of images and texts, natural language processing, and other tasks makes use of TensorFlow particularly well. TensorFlow is frequently used in both academics and industry to create cutting-edge deep learning models.

_Keras:_
Keras is a Python library for machine learning. For the construction and training of deep learning models, it offers a high-level API. Building neural networks for image and text classification, natural language processing, and other applications is particularly advantageous when using Keras. Modern deep learning models are created using the Keras framework, which is widely used in both industry and academics.

_Statsmodels:_
Statsmodels is a Python library for statistical analysis. For regression analysis, time series analysis, hypothesis testing, and other uses, it offers a variety of functions. Statsmodels is very helpful for performing statistical analysis on experimental data.
IDEs and environments for data science

**_The Anaconda platform_**
Data scientists, analysts, and developers can effectively organize and deploy their data science projects using the Anaconda data science platform. Users may execute data analysis, machine learning, and deep learning on this well-liked open-source platform all at once. For data scientists looking for a more efficient approach to work with data, Anaconda is a powerful resource.
Anaconda offers a complete collection of data science tools and libraries, including machine learning, data processing, and data visualization. Additionally, it offers a simple interface for developing and implementing data science projects. An entire environment for data science is provided by Anaconda, which is built on top of the popular computer language Python.

_Anaconda Distribution _ - The basis of the Anaconda platform is Anaconda Distribution. Users may simply manage and deploy their data science projects using this package management and environment manager. Around 1,500 packages are included in the Anaconda Distribution, including well-known ones like NumPy, pandas, Matplotlib, and Scikit-learn. Users can save time and effort by using these pre-installed, ready-to-use packages instead of setting up their environments from scratch.

_Anaconda Navigator_ - Users can manage their Anaconda environment with the aid of the graphical user interface (GUI) known as Anaconda Navigator. It offers a simple method for starting applications, controlling environments, and getting documentation. Users may create and manage new environments, install packages, and start Jupyter notebooks with Anaconda Navigator.

_Visual studio code_
Visual Studio Code is a free, open-source code editor developed by Microsoft. It is commonly used for coding and debugging applications in various programming languages. It also supports a variety of extensions and plugins that make it an excellent choice for data science.
Visual Studio Code for data science allows you to analyze, manipulate and visualize data. With the help of various extensions and plugins, you can work with different data formats, perform statistical analysis, create data visualizations, and more. Some of the popular extensions for data science in Visual Studio Code include:
1. Python: This extension provides features like syntax highlighting, code completion, and debugging for Python.
2. Jupyter: This extension allows you to create and edit Jupyter notebooks directly within Visual Studio Code.
3. R: This extension provides features like syntax highlighting, code completion, and debugging for R.
4. Data Preview: This extension allows you to preview data files (CSV, JSON, XML, etc.) within Visual Studio Code.
5. SQL Server: This extension provides features like syntax highlighting, code completion, and debugging for SQL Server.
Overall, Visual Studio Code is an excellent choice for data science, as it provides a comprehensive set of tools and features for working with data.

_Jupyter Notebook_
Data scientists often create, share, and document their data analysis processes using the robust open-source Jupyter Notebook application. Users can develop and share live code, equations, visualizations, and narrative text in a single document with this web-based interactive computing environment. Several programming languages, including Python, R, Julia, and many others, are supported.

An excellent environment for data science tasks like data cleaning, data visualization, statistical modeling, and machine learning is offered by Jupyter Notebook. Data scientists may write and run code, interactively explore data, and quickly produce visualizations with Jupyter Notebook. The outputs of the code cells are displayed below them as they are executed one at a time, making it simple to follow changes and spot problems in the code.
The actual strength of Jupyter Notebook rests in its capacity to combine text, code, and visuals into a single file. Data scientists can simply share their findings with others and record their code thanks to this functionality. Also, data scientists may quickly replicate their findings and collaborate with others in real-time using Jupyter Notebook.

Jupyter Notebook is a crucial tool for data science since it makes it easier to create and share processes for data analysis. It is also commonly used in research and teaching since it makes it simple for researchers and students to comprehend and experiment with data analysis approaches. Because of its adaptability and usability, it is a crucial tool for data scientists who want to design efficient data analysis workflows.
**_Get started_**
If you are interested in pursuing a career in data science, here are some steps you can take to get started.

1. Build a strong foundation in mathematics and statistics
Data science is all about analyzing and interpreting data, which requires a solid understanding of mathematics and statistics. To build a strong foundation in these areas, take courses in calculus, linear algebra, probability, and statistics. This will help you understand the underlying principles and concepts of data science.

2. Learn programming languages
Data science involves working with large datasets, which requires programming skills. Some of the popular programming languages used in data science are Python, R, and SQL. It is essential to learn at least one of these languages to start your career in data science. You can take online courses, attend workshops, or join coding bootcamps to learn these programming languages.

3. Gain practical experience
Practical experience is crucial for a career in data science. It is essential to work on real-world projects to apply your theoretical knowledge and gain practical experience. You can work on personal projects, participate in hackathons, or take internships to gain practical experience.

4. Get certified
Certifications can help you stand out in a crowded job market. There are various certifications available in data science, such as Microsoft Certified: Data Analyst Associate, IBM Data Science Professional Certificate, and Google Data Analytics Professional Certificate. These certifications can demonstrate your knowledge and skills to potential employers.

5. Network and attend events
Networking is a great way to connect with other professionals in the field and learn about job opportunities. Attend data science events, such as conferences, meetups, and workshops, to meet other professionals in the field. You can also join online communities, such as LinkedIn groups or Reddit communities, to connect with other data science professionals.

6. Apply for internships or entry-level jobs
Getting your foot in the door can be challenging, but internships or entry-level jobs can be a great way to start your career in data science. These opportunities can provide you with hands-on experience and help you build a portfolio of work that you can showcase to potential employers.

In conclusion, pursuing a career in data science requires a strong foundation in mathematics and statistics, programming skills, practical experience, certifications, networking, and applying for internships or entry-level jobs. By following these steps, you can get started on your path to a rewarding career in data science.