AI News, Everyone can do Data Science in Python

Everyone can do Data Science in Python

Data Science is a hot topic, and most data scientist use either Python or R to do their jobs as main scripting language.

Being data scientist for the last 2 years, all of them using Python, I’ve come across many different problems and needs on how to wrangle data, clean data, report on it and make predictions. In

Being data scientist for the last 2 years, all of them using Python, I’ve come across many different problems and needs on how to wrangle data, clean data, report on it and make predictions. In

Python libraries and packages for Data Scientists (the 5 most important ones)

And yet today it’s one of the best languages for statistics, machine learning, and predictive analytics as well as simple data analytics tasks.

It’s an open-source language, and data professionals started creating tools for it to complete data tasks more efficiently.

With pandas, you can load your data into data frames, you can select columns, filter for specific values, group by values, run functions (sum, mean, median, min, max, etc.), merge dataframes and so on.

Data visualization helps you to better understand your data, discover things that you wouldn’t discover in raw format and communicate your findings more efficiently to others. The

I wouldn’t say it’s easy to use… But usually if you save for yourself the 4 or 5 most commonly used code blocks for basic line charts and scatter plots, you can create your charts pretty fast.

Scikit-Learn has several methods, basically covering everything you might need in the first few years of your data career: regression methods, classification methods, and clustering, as well as model validation and model selection.

it’s rather the data cleaning, the data formatting, the data preparation, and finding the right input values and the right model.

Secondly, make sure you understand the theory and the mathematical background of the different prediction and classification models, so you know what happens with your data when you apply them.

And one of these components is the Scipy library itself, which provides efficient solutions for numerical routines (the math stuff behind machine learning models).

In this article, I won’t cover them because I think, for a start, it’s worth taking time to get familiar with the above mentioned five libraries.

First of all, you have to set up a basic data server by following my original How to install Python, R, SQL and bash to practice data science article.

Just follow these five steps: Once you have them installed, import them (or specific modules of them) into your Jupyter notebook by using the right import statements.

If you want to get hands-on experience and practice your data science skill with true-to-life tasks of a true-to-life startup’s true-to-life dataset, check out my new video course: The Junior Data Scientist’s First Month.

9 Must-have skills you need to become a Data Scientist, updated

Education Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist.

Apart from classroom learning, you can practice what you learned in the classroom by building an app, starting a blog or exploring data analysis to enable you to learn more.

A study carried out by CrowdFlower on 3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating.

As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers, this is where Hadoop comes in.

SQL Database/Coding Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL.

SQL (structured query language) is a programming language that can help you to carry out operations like add, delete and extract data from a database.

If you want to stand out from other data scientists, you need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression etc.

Kaggle, in one of its surveys, revealed that a small percentage of data professionals are competent in advanced machine learning skills such as Supervised machine learning, Unsupervised machine learning, Time series, Natural language processing, Outlier detection, Computer vision, Recommendation engines, Survival analysis, Reinforcement learning, and Adversarial learning.

Examples include videos, blog posts, customer reviews, social media posts, video feeds, audio etc.

 As a data scientist, you need to be able to ask questions about data because data scientists spend about 80 percent of their time discovering and preparing data.

You need to regularly update your knowledge by reading contents online and reading relevant books on trends in data science.

Business acumen  To be a data scientist you’ll need a solid understanding of the industry you’re working in, and know what business problems your company is trying to solve.

In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.

Communication skills  Companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments.

A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately.

You will have to work with company executives to develop strategies, work product managers and designers to create better products, work with marketers to launch better-converting campaigns, work with client and server software developers to create data pipelines and improve workflow.

Essentially, you will be collaborating with your team members to develop use cases in order to know the business goals and data that will be required to solve problems.

You will need to know the right approach to address the use cases, the data that is needed to solve the problem and how to translate and present the result into what can easily be understood by everyone involved.

Resources I’m sure there are items I may have missed, so if there’s a crucial skill or resource you think would be helpful to any data science hopefuls, feel free to share it in the comments below!

A Complete Tutorial to Learn Data Science with Python from Scratch

After working on SAS for more than 5 years, I decided to move out of my comfort zone. Being a data scientist, my hunt for other useful tools was ON!

But, over the years, with strong community support, this language got dedicated library for data analysis and predictive modeling.

Due to lack of resource on python for data science, I decided to create this tutorial to help many others to learn python faster. In this tutorial, we will take bite sized information about how to use Python for Data Analysis, chew it till we are comfortable and practice it at our own end.

There are 2 approaches to install Python: Second method provides a hassle free installation and hence I’ll recommend that to beginners.

The imitation of this approach is you have to wait for the entire package to be upgraded, even if you are interested in the latest version of a single library.

It provides a lot of good features for documenting while writing the code itself and you can choose to run the code in blocks (rather than the line by line execution) We will use iPython environment for this complete tutorial.

The most commonly used construct is if-else, with following syntax: For instance, if we want to print whether the number N is even or odd: Now that you are familiar with Python fundamentals, let’s take a step further.

What if you have to perform the following tasks: If you try to write code from scratch, its going to be a nightmare and you won’t stay on Python for more than 2 days!

Following are a list of libraries, you will need for any scientific computations and data analysis: Additional libraries, you might need: Now that we are familiar with Python fundamentals and additional libraries, lets take a deep dive into problem solving through Python.

We will now use Pandas to read a data set from an Analytics Vidhya competition, perform exploratory analysis and build our first basic categorization algorithm for solving this problem.

The essential difference being that column names and row numbers are known as column and row index, in case of dataframes.

To begin, start iPython interface in Inline Pylab mode by typing following on your terminal / windows command prompt: This opens up iPython notebook in pylab environment, which has a few useful libraries already imported.

You can check whether the environment has loaded correctly, by typing the following command (and getting the output as seen in the figure below):

describe() function would provide count, mean, standard deviation (std), min, quartiles and max in its output (Read this article to refresh basic statistics to understand population distribution) Here are a few inferences, you can draw by looking at the output of describe() function: Please note that we can get an idea of a possible skew in the data by comparing the mean to the median, i.e.

The frequency table can be printed by following command: Similarly, we can look at unique values of port of credit history.

Now we will look at the steps required to generate a similar insight using Python. Please refer to this article for getting a hang of the different data manipulation techniques in Pandas.

If you have not realized already, we have just created two basic classification algorithms here, one based on credit history, while other on 2 categorical variables (including gender).

Next let’s explore ApplicantIncome and LoanStatus variables further, perform data munging and create a dataset for applying various modeling techniques.

Let us look at missing values in all the variables because most of the models don’t work with missing data and even if they do, imputing them helps more often than not.

So, let us check the number of nulls / NaNs in the dataset This command should tell us the number of missing values in each column as isnull() returns 1, if the value is null.

the simplest being replacement by mean, which can be done by following code: The other extreme could be to build a supervised learning model to predict loan amount on the basis of other variables and then use age along with other variables to predict survival.

Since, the purpose now is to bring out the steps in data munging, I’ll rather take an approach, which lies some where in between these 2 extremes.

This can be done using the following code: Now, we will create a Pivot table, which provides us median values for all the groups of unique values of Self_Employed and Education features.

Next, we define a function, which returns the values of these cells and apply it to fill the missing values of loan amount: This should provide you a good way to impute missing values of loan amount.

So instead of treating them as outliers, let’s try a log transformation to nullify their effect: Looking at the histogram again:

For example, creating a column for LoanAmount/TotalIncome might make sense as it gives an idea of how well the applicant is suited to pay back his loan.

After, we have made the data useful for modeling, let’s now look at the python code to create a predictive model on our data set.

One way would be to take all the variables into the model but this might result in overfitting (don’t worry if you’re unaware of this terminology yet).

In simple words, taking all variables might result in the model understanding complex relations specific to the data and will not generalize well.

Accuracy : 80.945% Cross-Validation Score : 80.946% Accuracy : 80.945% Cross-Validation Score : 80.946% Generally we expect the accuracy to increase on adding variables.

Accuracy : 81.930% Cross-Validation Score : 76.656% Here the model based on categorical variables is unable to have an impact because Credit History is dominating over them.

Let’s try a few numerical variables: Accuracy : 92.345% Cross-Validation Score : 71.009% Here we observed that although the accuracy went up on adding variables, the cross-validation error went down.

Also, we will modify the parameters of random forest model a little bit: Accuracy : 82.899% Cross-Validation Score : 81.461% Notice that although accuracy reduced, but the cross-validation score is improving showing that the model is generalizing well.

You would have noticed that even after some basic parameter tuning on random forest, we have reached a cross-validation accuracy only slightly better than the original logistic regression model.

I am sure this not only gave you an idea about basic data analysis methods but it also showed you how to implement some of the more sophisticated techniques available today.

If you come across any difficulty while practicing Python, or you have any thoughts / suggestions / feedback on the post, please feel free to post them through comments below.

The Life of a Data Scientist

They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, manage and organize them.

Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges.

For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging.

A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.

Broadly speaking, you have 3 education options if you’re considering a career as a data scientist: Academic qualifications may be more important than you imagine.

To avoid wasting time on poor quality certifications, ask your mentors for advice, check job listing requirements and consult articles like Tom’s IT Pro “Best Of”

This includes the framing of business and analytics problems, data and methodology, model building, deployment and life cycle management.

Requirements: The EMCDS certification training will enable you to learn how to apply common techniques and tools required for big data analytics.

$163,132 Some data scientists get their start working as low-level Data Analysts, extracting structured data from MySQL databases or CRM systems, developing basic visualizations or analyzing A/B test results.

you could think about building/engineering/architecture jobs such as: Companies of every size and industry – from Google, LinkedIn and Amazon to the humble retail store – are looking for experts to help them wrestle big data into submission.

data scientists may find themselves responsible for financial planning, ROI assessment, budgets and a host of other duties related to the management of an organization.

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