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Not having much information about the distribution of a random variable can become a major problem for data scientists and statisticians. Consider, a researcher trying to understand the distribution of Choco-chips in a cookie (a very popular example of Poisson distribution). The researcher is well aware that the distribution of Choco-chips follows a Poisson distribution, but does not know how to estimate the parameter λ of the distribution.

A parameter is essentially a numerical characteristic of a distribution (or any statistical model in general). Normal distributions have µ & σ as parameters, uniform distributions have a & b as parameters, and binomial distributions have n & p as parameters. These numerical characteristics are vital for understanding the size, shape, spread, and other properties of a distribution. In the absence of the true value of the parameter, it seems that the researcher may not be able to continue her investigation. But that’s when estimators step in.

Estimators are functions of random variables that can help us find approximate values for these parameters. Think of these estimators like any other function, that takes an input, processes it, and renders an output. So, the process of estimation goes as follows:

1) From the distribution, we take a series of random samples.

2) We input these random samples into the estimator function.

3) The estimator function processes it and gives a set of outputs.

4) The expected value of that set is the approximate value of the parameter.

Example

Let’s take an example. Consider a random variable X showing a uniform distribution. The distribution of X can be represented as U[0, θ]. This has been plotted below:

(Figure A)

We have the random variable X and its distribution. But we don’t know how to determine the value of θ. Let’s use estimators. There are many ways to approach this problem. I’ll discuss two of them:

1) Using Sample Mean

We know that for a U[a, b] distribution, the mean µ is given by the following equation:

For U[0, θ] distribution, a = 0 & b = θ, we get:

Thus, if we estimate µ, we can estimate θ. To estimate µ, we use a very popular estimator called the sample mean estimator. The sample mean is the sum of the random sample value drawn divided by the size of the sample. For instance, if we have a random sample S = {4, 7, 3, 2}, then the sample mean is (4+7+3+2)/4 = 4 (the average value). In general, the sample mean is defined using the following notation:

Here, µ-hat is the sample mean estimator & n is the size of the random sample that we take from the distribution. A variable with a hat on top of it is the general notation for an estimator. Since our unknown parameter θ is twice of µ, we arrive at the following estimator for θ:

We take a random sample, plug it into the above estimator, and get a number. We repeat this process and get a set of numbers. The following figure illustrates the process:

(Figure B)

The lines on the x-axes correspond to the values present in the sample taken from the distribution. The red lines in the middle indicate the average value of the sample, and the red lines at the end are twice that average value i.e., the expected value of θ for one sample. Many such samples are taken, and the estimated value of θ for each sample is noted. The expected value/mean of that set of numbers gives the final estimate for θ. It can be mathematically proved (using properties of expectation):

It is seen that the expectation of the estimator is equal to the true value of the parameter. This amazing property that certain estimators have is called unbiasedness, which is a very useful criterion for assessing estimators.

2) Maximum Value Method

This time, instead of using mean, we’ll use order statistics, particularly the nth order statistic. The nth order statistic is defined as the nth smallest value of a random sample of size n. In other words, it’s the maximum value of a random sample. For instance, if we have a random sample S = {4, 7, 3, 2}, then the nth order statistic is 7 (the largest value). The estimator is now defined as follows:

We follow the same procedure- take random samples, input them, collect the output and find the expectation. The following figure illustrates the process:

(Figure C)

As noted previously, the lines on the x-axes are the values present in one sample. The red lines at the end are the maximum value for that sample i.e., the nth order statistic. Two random samples are shown for reference. However, we need to take much larger samples. Why? To prove it, we’ll use the general expression for the PDF (Probability Distribution Function) of nth order statistics for U[a, b] distribution:

For U[0, θ] distribution, a = 0 & b = θ, we get:

Using the integral form of expectation of a continuous variable,

Does that mean that we cannot use this estimator? Certainly not. As discussed earlier, the estimator bias can be significantly lowered by taking large n. For large values of n, n = n+1 (approximately). Thus, we get:

The Bottom Line

Hence, we have successfully solved our problem through estimators. We also learned a very important property of estimators- unbiasedness. While this may have been an extensive read, it’s imperative to acknowledge that the study of estimators is not restricted to just the above-explained concepts. Various other properties of estimators such as their efficiency, robustness, mean squared error, and consistency are also vital to deepen our understanding of them.

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## An Introduction On Etl Tools For Beginners

This article was published as a part of the Data Science Blogathon.

Introduction on ETL Tools

The amount of data being used or stored in today’s world is extremely huge. Many companies, organizations, and industries store the data and use it as per the requirement. While handling this huge amount of data, one has to follow certain steps. Whenever we start working with data, specific words/terms come to our minds. For example, data warehouses, databases, attributes, ETL, data filtering, etc. In this article, we are going to have a brief introduction to one such term named ETL.

What is ETL?

ETL stands for extract, transform, load. Let’s see these terms one by one.

Extract:

It means extracting data from its source which can be an application or another database. Extraction can be divided further into two types:

a) Partial extraction

b) Full extraction

Transform:

It means transforming the raw data which has been extracted from sources. Transforming includes filtering the data, cleaning the data, mapping and transforming data, etc. This step may include some simple changes to source data or some multiprocessing which includes multiple data sources.

It means converting transformed data into the target database. The target databases can be DataMart, Data Warehouses, or databases. These destination sources are used for analytical purposes, planning business strategies, etc.

In short, the ETL tool performing the above three steps ensures that the data is complete, usable and as per the requirement for further processes like analysis, reporting, and machine learning/artificial intelligence.

Where to use ETL? Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence include a lot of data. The cloud is the only feasible solution to store this huge amount of data. Besides, both of these techniques require large datastores for analytical model building and training. Cloud-based ETL tools are useful here to both migrate large amounts of data to the cloud and transform them to be analytics-ready.

Data Warehousing

Many of the enterprisers use ETL tools to collect data from various sources, then transform it into a consistent format and load it into a data warehouse. Then business intelligence teams can analyze the data stored in data warehouses for business purposes. Data warehouses play an important role in various business intelligence functions. Also, they act as a key component in creating dashboards/reports.

Data Migration

Data Migration is the process of transferring data from one system to another while changing the storage, database, or application. ETL plays an important role here. ETL tools help in integrating the contextual data which can be further used by business analysts/marketers for personalized marketing, improving the user experience, or in understanding customer behavior.

Why use ETL?

There are plenty of reasons why ETL is being used. ETL provides a method of moving data from various sources into a data warehouse. It helps companies to analyze their business data and further helps in making critical business decisions or planning marketing strategies. Sample data comparison can be performed between the source and target systems with the help of ETL. ETL offers deep historical context as well, which can be used for various business purposes. Besides, ETL helps to migrate the data into a data warehouse.

ETL Challenges Loss of Data/Irrelevant data

There is a possibility that some of the data is lost or data gets corrupted because some steps are not performed correctly while transforming or loading the data. Some irrelevant data can also be there due to such mistakes.

Disparate Data Sources

Sometimes the data sources may not be aligned or mapped properly. In such cases, dealing with these data sources becomes a big challenge.

Problems with data quality and integrity

Sometimes while normalizing or transforming the data, there can be performance issues. This may lead to loss of data quality or data integrity. Hence, it becomes another big challenge while using ETL.

ETL Tools

ETL Tools can be of different types. Some software companies develop and sell commercial ETL software products. They can be included in Enterprise Software ETL Tools. Examples of such tools are as follows:

1. SAP Data Services

2. Oracle Data Integrator

3. IBM ETL Tool

4. SAS Data Manager

Another type of ETL tool is open-source ETL tools. For example, Hadoop. Hadoop is a general-purpose distributed computing platform. It can be used to store, manipulate and analyze data. These products are free to use.

The Third type of ETL Tool is Custom ETL Tools. These are simple programming languages that are being used by many companies to write their own ETL tools. These programming languages include Python, Java, SQL, Spark, and Hadoop. These types of ETL tools provide the greatest flexibility. Although, they require a lot of effort.

Apart from these tools, Amazon AWS, Google Cloud Platform, and Microsoft Azure provide their own ETL capabilities as cloud services.

Conclusion

ETL model is being used by many companies for more than 30 years. Many companies read data from various sources, transform this extracted data using different techniques and then load it into the destination sources/systems. Though, some challenges to be faced while using/testing ETL tools, the ETL Tools are in use for many years. Companies use ETL to safely move their data from one system to another.

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## Understanding Operations Security (Opsec) An Introduction For Beginners

Introduction

In today’s hyper-connected world, keeping sensitive information secure is more critical than ever. Enter Operations Security (OPSEC), a risk management process designed to protect your organization’s vital data from falling into the wrong hands.

Whether you’re a business leader or an individual interested in safeguarding your personal information, understanding the basics of OPSEC is essential. In this introductory guide, we’ll break down what OPSEC is, explore its five-step process, and offer best practices for implementation – empowering you with practical knowledge that can help prevent costly security breaches. Dive into this beginner-friendly article to uncover how OPSEC can enhance your privacy and overall security strategy.

What is Operations Security (OPSEC)?

Operations Security (OPSEC) is a process that ensures the protection of sensitive information by identifying critical data, analyzing threats and vulnerabilities, assessing potential risks, implementing countermeasures, and regularly evaluating the effectiveness of security measures.

Definition and Purpose of OPSEC

The core concept behind OPSEC originated in the United States military as a method to protect mission-critical information from enemy forces. Over time, its application has expanded beyond the military realm to encompass private businesses and individuals seeking to safeguard intellectual property, trade secrets, personal identity details, and other types of sensitive data. In today’s interconnected world where threats of cyber attacks loom large and digital espionage knows no boundaries; implementing effective OPSEC measures becomes crucial for maintaining confidentiality across various domains be it physical premises security or protection against online intrusions.

Importance of OPSEC for Personal and Organizational Security

OPSEC is crucial for both personal and organizational security. Individuals benefit from OPSEC because it protects sensitive information such as financial information, social security numbers, and personal connections from cybercriminals who may use it to commit identity theft or institutional fraud.

Hackers may also attempt to exploit system vulnerabilities by getting unauthorised access to an individual’s internet accounts or computer systems.

OPSEC guarantees that essential information such as trade secrets, intellectual property, and sensitive data are not compromised in enterprises. Breaches in security can lead to significant losses of revenue and harm brand reputation if confidential client data is exposed. Cyberattacks could also result in operational downtime and legal repercussions due to noncompliance with data protection laws.

The Five Steps of OPSEC

The five steps of OPSEC include identifying critical information, analyzing threats and vulnerabilities, assessing risks and potential impacts, implementing countermeasures, and reviewing and evaluating the effectiveness of OPSEC.

Identifying Critical Information

Identifying critical information is the first step in OPSEC, and it involves determining what information needs to be kept confidential. This may include everything from sensitive data about clients or customers to classified government documents. It’s important to note that not all information is equal in importance, and some may require stricter security protocols than others.

For example, a law firm might consider client files containing personal identification information (PII) as critical information that needs protection against cyber-attacks or unauthorized access. On the other hand, less sensitive communications like internal memos may not need as strict precautions.

It’s vital to understand what your organization considers critical information and where it resides. This includes everything from paper records stored in filing cabinets to digital assets such as databases or cloud servers. Once you’ve identified these crucial assets of your organization, you can begin analyzing possible threats and vulnerabilities they pose while developing effective countermeasures for safeguarding them against potential risks.

Analyzing Threats and Vulnerabilities

For example, a small business might consider theft or data breaches as potential threats to the confidentiality of its customer database. Meanwhile, leaving passwords written on sticky notes might be a vulnerability that could lead to unauthorized access. By analyzing these types of scenarios, a business can develop targeted solutions and prevent or mitigate risks before they become exploitable.

Overall, threat analysis involves determining who might want your information and how they would go about obtaining it, while vulnerability assessment entails assessing your current security measures’ effectiveness against identified threats. The more detailed this analysis is at each point in time — given evolving technologies used by attackers — the more effective an OPSEC program will be in protecting sensitive data from being compromised.

Assessing Risks and Potential Impacts

Assessing risks and potential impacts is the third step in the OPSEC process. This involves evaluating the likelihood of a threat exploiting vulnerabilities to access critical information and assessing the impact that a breach could have on organizational security. It’s crucial to understand potential risks and their possible consequences beforehand, as this helps formulate effective countermeasures.

Implementing Countermeasures

Implementing countermeasures is a critical step in the OPSEC process. Here are some best practices for implementing effective countermeasures −

Develop a security plan that identi

fies potential threats and vulnerabilities.

Select and implement appropriate countermeasures to mitigate risks and protect against threats.

Educate all personnel on the use of these countermeasures, including proper handling of sensitive information.

Regularly review and update countermeasures to ensure they remain effective in preventing information leaks or breaches of confidentiality.

Limit access to sensitive information only to those with an established need-to-know.

Implement mechanisms for detecting, reporting, and responding to suspected security violations or incidents promptly.

By following these best practices, organizations can effectively safeguard their critical information from unauthorized access or disclosure while promoting operational security awareness throughout the organization.

Reviewing and Evaluating OPSEC Effectiveness

Once you have implemented OPSEC measures, it’s essential to review and evaluate their effectiveness regularly. This step helps identify vulnerabilities or gaps in the security process that could compromise critical information. By reviewing and evaluating OPSEC, you can ensure that your organization’s confidentiality is maintained.

Some examples of assessing the effectiveness of OPSEC include conducting penetration tests, monitoring access controls, or analyzing security logs for anomalies. Regular reviews ensure that new threats or potential risks are considered and addressed promptly.

Remember that no security plan is foolproof; therefore, continuous evaluation is essential for effective risk management. Incorporating employee feedback can also help refine existing procedures with real-life experiences providing a valuable source of insight into improving operational security practices within an organization.

Conclusion

## Introduction To Master Data In Sap

What is Master Data?

Data stored in SAP R/3 is categorized as

Master Data and

Transactional Data.

If you are producing, transferring stock, selling, purchasing, doing physical inventory, whatever your activity may be, it requires certain master data to be maintained.

Example of Master Data

Material master data

Customer master data

Vendor master data

Pricing/conditions master data

Warehouse management master data (storage bin master data)

The ones we will focus in MM module are material master and purchase info record.

Material Master: What you should know about material master?

Material in SAP is a logical representation of certain goods or service that is an object of production, sales, purchasing, inventory management etc. It can be a car, a car part, gasoline, transportation service or consulting service, for example.

InInIn All the information for all materials on their potential use and characteristics in SAP are called material master. This is considered to be the most important master data in SAP (there are also customer master data, vendor master data, conditions/pricing master data etc), and all the processing of the materials are influenced by material master. That is why it’s crucial to have a precise and well maintained material master.

In order to be confident in your actions you need to understand material master views and its implications on processes in other modules, business transactions and a few more helpful information like tables that store material master data, transactions for mass material maintenance (for changing certain characteristics for a large number of materials at once).

Material types

In SAP ERP, every material has a characteristic called “material type” which is used throughout the system for various purposes.

Why is it essential to differentiate between material types and what does that characteristic represent?

It can represent a type of origin and usage – like a finished product (produced goods ready for sale), semifinished product (used as a part of a finished product), trading goods (for resale), raw materials (used for production of semifinished and finished products) etc. These are some of the predefined SAP material types among others like food, beverages, service and many others.

We can define our custom material types if any of standard ones doesn’t fulfill our need.

Most used material types in standard SAP installation

What can be configured on material type level (possible differences between types)?

Material master views: It defines the views associated with a Material Type. For example, if we have a material type “FERT” assigned to our material Product 1000 – we don’t want to have Purchasing based views for that material because we don’t need to purchase our own product – it is configured on material type level.

Default price control: we can set this control to standard or moving average price (covered later in detail), but this can be changed in material master to override the default settings.

Default Item category group: used to determine item category in sales documents. It can be changed in material master to override the default settings.

internal/external purchase orders, special material types indicators, and few more.

Offered material types in MM01 transaction

So material type is assigned to materials that have the same basic settings for material master views, price control, item category group and few other. Material Type can be assigned during the creation of the material in t-code MM01 (covered in detail later)

Where can we find a complete list of materials with their respective material type?

There are numerous transactions for this. The raw data itself is stored in MARA table

(you can view table contents with t-code SE16 or SE16N – newest version of the transaction), but in some systems these t-codes aren’t allowed for a standard user. In such cases, we can easily acquire the list with t-code MM60 (Material list). MM60 is used particularly often as it displays a lot of basic material characteristics.

Selection screen – you can enter only the material number:

Selection screen for MM60 transaction

We can see that material 10410446 in plant AR01 is of type FERT (finished product).

MM60 report results with the export button highlighted

Using the toolbar button highlighted on screen, we can export the list of materials we have selected on screen.

Material group

Another characteristic SAP material is assigned during it’s creation is “material group”, which can represent a group or subgroup of materials based on certain criteria.

Which criteria can be used to create material groups?

Any criteria that suit your needs for reporting purposes is right for your system. You may group materials by the type of raw material used to produce it (different kinds of plastics used in the production process), or you can divide all services into consulting services (with different materials for SAP consulting, IT consulting, financial consulting etc), transportation services (internal transport, international transport), you can also group by production technique (materials created by welding, materials created by extrusion, materials created by injection etc). Grouping depends mainly on the approach your management chooses as appropriate, and it’s mainly done during the implementation, rarely changes in a productive environment.

Assigned material group in material master

In addition, there is a material hierarchy (used mostly in sales & distribution) that can also be used for grouping, but it’s defined almost always according to sales needs as it is used for defining sales conditions (standard discounts for customers, additional discounts, special offers).

On the other hand, material group is mainly used in PP and MM module.

If you need to display material groups for multiple materials, you can use already mentioned t-code MM60. You just need to select more materials in selection criteria.

Material group in report MM60

Material group is easily subject to mass maintenance via transaction MM17. More on that in the material master editing section.

## Top 15 Free Data Science Courses To Kick Start Your Data Science Journey!

Overview

Here is a list of 15 Free Data Science Courses to get you going initially

These are well-curated courses. Please probe the resources attached to these free data science courses to understand them better

Introduction

It is Data Science, not Rocket Science.

Due to the democratization of AI and ML, the data science field is undergoing massive growth. A lot of long shot applications like self-driven cars, smart AI assistants have come to life. It is really exciting!

I have come across hundreds of data science aspirants who really want to pursue this field but aren’t able to navigate their way through this uncertain path. It is not their fault. The majority of people haven’t graduated in this field. So getting back to the main question – How do build a successful career in data science and more importantly, what are the necessary resources to do so?

In this article, I am listing down 15 free courses, starting with beginner courses that will help you navigate your way through a data science career and then jump into each important machine learning algorithm. I have also mentioned a few project-based courses, this will surely help you in practical learning.

However, These free data science courses are not a substitute for a well-guided course. The AI and ML Blackbelt+ program is the leading industry course for data science. Along with 14+ courses and 39+ projects, it offers you –

1:1 Mentorships with Industry Practitioners

Comprehensive & Personalised Learning Path

Dedicated Interview Preparation & Support

You can check the entire program here.

List of Free Data Science Courses

Introduction to AI and ML

Python for Data Science

Pandas for Data Analysis

Sklearn

KNN

Regression

Decision Trees

Ensemble Learning

Naive Bayes

SVM

Evaluation Metrics

Introduction to NLP

Getting started with Neural Networks

Loan Prediction Problem

Winning Data Science Competitions

“The AI revolution is here – are you prepared to integrate it into your skillset? How can you leverage it in your current role? What are the different facets of AI and ML?”

Artificial Intelligence and Machine Learning have become the centerpiece of strategic decision making for organizations. They are disrupting the way industries and roles function – from sales and marketing to finance and HR, companies are betting big on AI and ML to give them a competitive edge.

And this, of course, directly translates to their hiring. Thousands of vacancies are open as organizations scour the world for AI and ML talent. There hasn’t been a better time to get into this field!

This course helps you answer all the conceptual questions you might have about building a successful career in data science and machine learning.

You can find the course material here.

Do you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?

You have come to just the right place!

Most industry experts recommend starting your Data Science journey with Python

Across the biggest companies and startups, Python is the most used language for Data Science and Machine Learning Projects

Stackoverflow survey for 2023 had Python outrank Java in the list of most loved languages

Python is a very versatile language since it has a wide array of functionalities already available. The sheer range of functionalities might sound too exhaustive and complicated, you don’t need to be well-versed with them all.

Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist’s skill set.

It consistently ranks top in global data science surveys and its widespread popularity will only keep on increasing in the coming years.

Over the years, with strong community support, this language has obtained a dedicated library for data analysis and predictive modeling.

You can find the course material here.

Now that we have the basics cleared up – Let’s move to specialized courses for machine learning and its libraries in Python.

Pandas is one of the most popular Python libraries in data science. In fact, Pandas is among those elite libraries that draw instant recognition from programmers of all backgrounds, from developers to data scientists.

According to a recent survey by StackOverflow, Pandas is the 4th most used library/framework in the world!

This free course will introduce you to the world of Pandas in Python, how you can use Pandas to perform data analysis and data manipulation. The perfect starting course for Python and Pandas beginners!

Scikit-learn, or sklearn for short, is the first Python library we turn to when building machine learning models. Sklearn is unanimously the favorite Python library among data scientists. As a newcomer to machine learning, you should be comfortable with sklearn and how to build ML models, including:

Linear Regression using sklearn

Logistic Regression using sklearn, and so on.

There’s no question – scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn (sklearn) ranks in the top echelon along with Pandas and NumPy.

We love the clean, uniform code, and functions that scikit-learn provides. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models using sklearn.

In short, sklearn is a must-know Python library for machine learning. Whether you want to build linear regression or logistic regression models, decision tree,s or a random forest, sklearn is your go-to library.

You can find the course material here.

K-Nearest Neighbor (KNN) is one of the most popular machine learning algorithms. As a newcomer or beginner in machine learning, you’ll find KNN to be among the easiest algorithms to pick up.

And despite its simplicity, KNN has proven to be incredibly effective at certain tasks in machine learning.

The KNN algorithm is simple to understand, easy to explain, and perfect to demonstrate to a non-technical audience (that’s why stakeholders love it!). That’s a key reason why it’s widely used in the industry and why you should know how the algorithm works.

You can find the course material here.

Linear regression and logistic regression are typically the first algorithms we learn in data science. These are two key concepts not just in machine learning, but in statistics as well.

Due to their popularity, a lot of data science aspirants even end up thinking that they are the only forms of regression! Or at least linear regression and logistic regression are the most important among all forms of regression analysis.

The truth, as always, lies somewhere in between. There are multiple types of regression apart from linear regression:

Ridge regression

Lasso regression

Polynomial regression

Stepwise regression, among others.

Linear regression is just one part of the regression analysis umbrella. Each regression form has its own importance and a specific condition where they are best suited to apply.

Regression analysis marks the first step in predictive modeling. The different types of regression techniques are widely popular because they’re easy to understand and implement using a programming language of your choice.

You can find the course material here.

Bonus: This free course comes with a degree as well.

A Decision Tree is a flowchart like structure, where each node represents a decision, each branch represents an outcome of the decision, and each terminal node provides a prediction/label.

This course covers the following topics –

Introduction

Terminologies

The different splitting criterion for decision tree-like Gini, chi-square

Implementation of the decision tree in Python

You can access the course here.

Ensemble learning is a powerful machine learning algorithm that is used across industries by data science experts. The beauty of ensemble learning techniques is that they combine the predictions of multiple machine learning models. You must have used or come across several of these ensemble learning techniques in your machine learning journey:

Bagging

Boosting

Stacking

Blending, etc.

These ensemble learning techniques include popular machine learning algorithms such as XGBoost, Gradient Boosting, among others. You must be getting a good idea of how vast and useful ensemble learning can be!

You can find the course material here.

Naive Bayes ranks in the top echelons of the machine learning algorithms pantheon. It is a popular and widely used machine learning algorithm and is often the go-to technique when dealing with classification problems.

The beauty of Naive Bayes lies in its incredible speed. You’ll soon see how fast the Naive Bayes algorithm works as compared to other classification algorithms. It works on the Bayes theorem of probability to predict the class of unknown datasets. You’ll learn all about this inside the course!

So whether you’re trying to solve a classic HR analytics problem like predicting who gets promoted, or you’re aiming to predict loan default – the Naive Bayes algorithm will get you on your way.

You can find the course material here.

Want to learn the popular machine learning algorithm – Support Vector Machines (SVM)? Support Vector Machines can be used to build both Regression and Classification Machine Learning models.

This free course will not only teach you the basics of Support Vector Machines (SVM) and how it works, it will also tell you how to implement it in Python and R.

This course on SVM would help you understand hyperplanes and Kernel tricks to leave you with one of the most popular machine learning algorithms at your disposal.

You can find the course material here.

Evaluation metrics form the backbone of improving your machine learning model. Without these evaluation metrics, we would be lost in a sea of machine learning model scores – unable to understand which model is performing well.

Wondering where evaluation metrics fit in? Here’s how the typical machine learning model building process works:

We build a machine learning model (both regression and classification included)

Get feedback from the evaluation metric(s)

Make improvements to the model

Use the evaluation metric to gauge the model’s performance, and

Continue until you achieve a desirable accuracy

Evaluation metrics, essentially, explain the performance of a machine learning model. An important aspect of evaluation metrics is their capability to discriminate among model results.

If you’ve ever wondered how concepts like AUC-ROC, F1 Score, Gini Index, Root Mean Square Error (RMSE), and Confusion Matrix work, well – you’ve come to the right course!

You can find the course material here.

Natural Language Processing is expected to be worth 30 Billion USD by 2024 with the past few years seeing immense improvements in terms of how well it is solving industry problems at scale.

This free course will guide you to take your first step into the world of natural language processing with Python and build your first sentiment analysis Model using machine learning.

From classifying images and translating languages to building a self-driving car, neural networks are powering the world around us.

Neural networks are the present and the future. The different neural network architectures like convolutional neural networks (CNN), recurrent neural networks (RNN), and others have altered the deep learning landscape.

This free course will give you a taste of what a neural network is, how it works, what are the building blocks of a neural network, and where you can use neural networks.

Do you need a free course which can help you solve data science problems practically? This amazing course will guide you in solving a real-life project.

This course is designed for people who want to solve binary classification problems. Classification is a skill every Data Scientist should be well versed in. In this course, you will get to solve a real-life case study of Dream Housing Finance.

There is no substitute for experience. And that holds true in Data Science competitions as well. These cut-throat hackathons require a lot of trial-and-error, effort, and dedication to reach the ranks of the elite.

This course is an amalgamation of various talks by top data scientists and machine learning hackers, experts, practitioners, and leaders who have participated and won dozens of hackathons. They have already gone through the entire learning process and they showcase their work and thought process in these talks.

This course features top data science hackers and experts, including Sudalai Rajkumar (SRK), Dipanjan Sarkar, Rohan Rao, Kiran R, and many more!

From effective feature engineering to choosing the right validation strategy, there is a LOT to learn from this course so get started today!

You can find the course material here.

End Notes

It is exciting to be in the data science industry. These free courses cover almost all the basics you will require to kickstart your career in data science.

I hope this helps you clear all the concepts. If you want to learn data science comprehensively then I have a great suggestion for you guys! The AI and ML Blackbelt+ program the industry leader in data science programs. Here you will not only get access to 14+ courses and 39+ projects but 1:1 mentorship sessions. The mentor will help you customize the learning path according to your career goals and make sure that you achieve them!

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## Comprehensive Learning Path – Data Science In Python

Journey from a Python noob to a Kaggler on Python

So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to Python for data science. This path provides a comprehensive overview of steps you need to learn to use Python for data science. If you already have some background, or don’t need all the components, feel free to adapt your own paths and let us know how you made changes in the path.

Reading this in 2023? We have designed an updated learning path for you! Check it out on our courses portal and start your data science journey today.

Step 0: Warming up

Why use Python?

or

How would Python be useful?

Watch the first 30 minutes of this talk from Jeremy, Founder of DataRobot at PyCon 2014, Ukraine to get an idea of how useful Python could be.

Step 1: Setting up your machine

Now that you have made up your mind, it is time to set up your machine. The easiest way to proceed is to just download Anaconda from chúng tôi . It comes packaged with most of the things you will need ever. The major downside of taking this route is that you will need to wait for Continuum to update their packages, even when there might be an update available to the underlying libraries. If you are a starter, that should hardly matter.

If you face any challenges in installing, you can find more detailed instructions for various OS here.

Step 2: Learn the basics of Python language

You should start by understanding the basics of the language, libraries and data structure. The free course by Analytics Vidhya on Python is one of the best places to start your journey. This course focuses on how to get started with Python for data science and by the end you should be comfortable with the basic concepts of the language.

Assignment: Take the awesome free Python course by Analytics Vidhya

Alternate resources: If interactive coding is not your style of learning, you can also look at The Google Class for Python. It is a 2 day class series and also covers some of the parts discussed later.

Step 3: Learn Regular Expressions in Python

You will need to use them a lot for data cleansing, especially if you are working on text data. The best way to learn Regular expressions is to go through the Google class and keep this cheat sheet handy.

Assignment: Do the baby names exercise

If you still need more practice, follow this tutorial for text cleaning. It will challenge you on various steps involved in data wrangling.

Step 4: Learn Scientific libraries in Python – NumPy, SciPy, Matplotlib and Pandas

This is where fun begins! Here is a brief introduction to various libraries. Let’s start practicing some common operations.

Practice the NumPy tutorial thoroughly, especially NumPy arrays. This will form a good foundation for things to come.

Next, look at the SciPy tutorials. Go through the introduction and the basics and do the remaining ones basis your needs.

If you guessed Matplotlib tutorials next, you are wrong! They are too comprehensive for our need here. Instead look at this ipython notebook till Line 68 (i.e. till animations)

Finally, let us look at Pandas. Pandas provide DataFrame functionality (like R) for Python. This is also where you should spend good time practicing. Pandas would become the most effective tool for all mid-size data analysis. Start with a short introduction, 10 minutes to pandas. Then move on to a more detailed tutorial on pandas.

You can also look at Exploratory Data Analysis with Pandas and Data munging with Pandas

If you need a book on Pandas and NumPy, “Python for Data Analysis by Wes McKinney”

There are a lot of tutorials as part of Pandas documentation. You can have a look at them here

Assignment: Solve this assignment from CS109 course from Harvard.

Step 5: Effective Data Visualization

Go through this lecture form CS109. You can ignore the initial 2 minutes, but what follows after that is awesome! Follow this lecture up with this assignment.

Step 6: Learn Scikit-learn and Machine Learning

Now, we come to the meat of this entire process. Scikit-learn is the most useful library on python for machine learning. Here is a brief overview of the library. Go through lecture 10 to lecture 18 from CS109 course from Harvard. You will go through an overview of machine learning, Supervised learning algorithms like regressions, decision trees, ensemble modeling and non-supervised learning algorithms like clustering. Follow individual lectures with the assignments from those lectures.

You should also check out the ‘Introduction to Data Science‘ course to give yourself a big boost in your quest to land a data scientist role.

If there is one book, you must read, it is Programming Collective Intelligence – a classic, but still one of the best books on the subject.

Additionally, you can also follow one of the best courses on Machine Learning course from Yaser Abu-Mostafa. If you need more lucid explanation for the techniques, you can opt for the Machine learning course from Andrew Ng and follow the exercises on Python.

Tutorials on Scikit learn

Step 7: Practice, practice and Practice

You now have all what you need in technical skills. It is a matter of practice and what better place to practice than compete with fellow Data Scientists on the DataHack platform. Go, dive into one of the live competitions currently running on DataHack and Kaggle and give all what you have learnt a try!

Step 8: Deep Learning

Now that you have learnt most of machine learning techniques, it is time to give Deep Learning a shot. There is a good chance that you already know what is Deep Learning, but if you still need a brief intro, here it is.

I am myself new to deep learning, so please take these suggestions with a pinch of salt. The most comprehensive resource is chúng tôi You will find everything here – lectures, datasets, challenges, tutorials. You can also try the course from Geoff Hinton a try in a bid to understand the basics of Neural Networks.

Get Started with Python: A Complete Tutorial To Learn Data Science with Python From Scratch

P.S. In case you need to use Big Data libraries, give Pydoop and PyMongo a try. They are not included here as Big Data learning path is an entire topic in itself.

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