Reports suggest that around 2.5 quintillion bytes of data are generated every single day. As the online usage growth increases at a tremendous rate, there is a need for immediate Data Science professionals who can clean the data, obtain insights from it, visualize it, train model and eventually come up with solutions using Big data for the betterment of the world.
By 2020, experts predict that there will be more than 2.7 million data science and analytics jobs openings. Having a glimpse of the entire Data Science pipeline, it is definitely tiresome for a single human to perform and at the same time excel at all the levels. Hence, Data Science has a plethora of career options that require a spectrum set of skill sets.
Let us explore the top 5 data science career options in 2019 (In no particular order).
1. Data Scientist
Data Scientist is one of the ‘high demand’ job roles. The day to day responsibilities involves the examination of big data. As a result of the analysis of the big data, they also actively perform data cleaning and organize the big data. They are well aware of the machine learning algorithms and understand when to use the appropriate algorithm. During the due course of data analysis and the outcome of machine learning models, patterns are identified in order to solve the business statement.
The reason why this role is so crucial in any organisation is that the company tends to take business decisions with the help of the insights discovered by the Data Scientist to have an edge over the company’s competitors. It is to be noted that the Data Scientist role is inclined more towards the technical domain. As the role demands a wide range of skill set, Data Scientists are one among the highest paid jobs.
Core Skills of a Data Scientist
Communication
Business Awareness
Database and querying
Data warehousing solutions
Data visualization
Machine learning algorithms
2. Business Intelligence Developer
BI Developer is a job role inclined more towards the Non-Technical domain but has a fair share of Technical responsibilities as well (if required) as a part of their day to day responsibilities. BI developers are responsible for creating and implementing business policies as a result of the insights obtained from the Technical team.
Apart from being a policymaker involving the usage of dedicated (or custom) Business Intelligence analytics tools, they will also have a fair share of coding in order to explore the dataset, present the insights of the dataset in a non-verbal manner. They help in bridging the gap between the technical team that works with the deepest technical understanding and the clients that want the results in the most non-technical manner. They are expected to generate reports from the insights and make it ‘less technical’ for others in the organisation. It is noted that the BI Developers have a deep understanding of Business when compared to Data Scientist.
Core Skills of a Business Analytics Developer
Business model analysis
Data warehousing
Design of business workflow
Business Intelligence software integration
3. Machine Learning Engineer
Once the data is clean and ready for analysis, the machine learning engineers work on these big data to train a predictive model that predicts the target variable. These models are used to analyze the trends of the data in the future so that the organisation can take the right business decisions. As the dataset involved in a real-life scenario would involve a lot of dimensions, it is difficult for a human eye to interpret insights from it. This is one of the reasons for training machine learning algorithms as it easily deals with such complex dataset. These engineers carry out a number of tests and analyze the outcomes of the model.
The reason for conducting constant tests on the model using various samples is to test the accuracy of the developed model. Apart from the training models, they also perform exploratory data analysis sometimes in order to understand the dataset completely which will, in turn, help them in training better predictive models.
Core Skills of Machine Learning Engineers
Machine Learning Algorithms
Data Modelling and Evaluation
Software Engineering
4. Data Engineer
The pipeline of any data-oriented company begins with the collection of big data from numerous sources. That’s where the data engineers operate in any given project. These engineers integrate data from various sources and optimize them according to the problem statement. The work usually involves writing queries on big data for easy and smooth accessibility. Their day to day responsibility is to provide a streamlined flow of big data from various distributed systems. Data engineering differs from the other data science careers as in, it is concentrated on the system and hardware that aids the company’s data analysis, rather than the analysis of data itself. They provide the organisation with efficient warehousing methods as well.
Core Skills of Data Engineer
Database Knowledge
Data Warehousing
Machine Learning algorithm
5. Business Analyst
Business Analyst is one of the most essential roles in the Data Science field. These analysts are responsible for understanding the data and it’s related trend post the decision making about a particular product. They store a good amount of data about various domains of the organisation. These data are really important because if any product of the organisation fails, these analysts work on these big data to understand the reason behind the failure of the project. This type of analysis is vital for all the organisations as it makes them understand the loopholes in the company. The analysts not only backtrack the loophole and in turn provide solutions for the same making sure the organisation takes the right decision in the future. At times, the business analyst act as a bridge between the technical team and the rest of the working community.
Core skills of Business Analyst
Business awareness
Communication
Process Modelling
Conclusion
The data science career options mentioned above are in no particular order. In my opinion, every career option in Data Science field works complimentary with one another. In any data-driven organization, regardless of the salary, every career role is important at the respective stages in a project.
Sifting through very large amounts of data for useful information. Data mining uses artificial intelligence techniques, neural networks, and advanced statistical tools. It reveals trends, patterns, and relationships, which might otherwise have remained undetected. In contrast to an expert system, data mining attempts to discover hidden rules underlying the data. Also called data surfing.
In this blog, we will be presenting a comprehensive detail about data mining. Additionally, this blog will help you to get into the details of data mining. Furthermore, it will help you to get the complete picture in one place!
What is Data Mining?
Data mining is not a new concept but a proven technology that has transpired as a key decision-making factor in business. There are numerous use cases and case studies, proving the capabilities of data mining and analysis. Yet, we have witnessed many implementation failures in this field, which can be attributed to technical challenges or capabilities, misplaced business priorities and even clouded business objectives. While some implementations battle through the above challenges, some fail in delivering the right data insights or their usefulness to the business. This article will guide you through guidelines for successfully implementing data mining projects.
Also, data mining is the process of uncovering patterns inside large sets of structured data to predict future outcomes. Structured data is data that is organized into columns and rows so that they can be accessed and modified efficiently. Using a wide range of machine learning algorithms, you can use data mining approaches for a wide variety of use cases to increase revenues, reduce costs, and avoid risks.
Also, at its core, data mining consists of two primary functions, description, for interpretation of a large database and prediction, which corresponds to finding insights such as patterns or relationships from known values. Before deciding on data mining techniques or tools, it is important to understand the business objectives or the value creation using data analysis. The blend of business understanding with technical capabilities is pivotal in making big data projects successful and valuable to its stakeholders.
Different Methods of Data Mining
Data mining commonly involves four classes of tasks [1]: (1) classification, arranges the data into predefined groups; (2) clustering, is like classification but the groups are not predefined, so the algorithm will try to group similar items together; (3) regression, attempting to find a function which models the data with the least error; and (4) association rule learning, searching for relationships between variables.
1. Association
Association is one of the best-known data mining technique. In association, a pattern is discovered based on a relationship between items in the same transaction. That’s is the reason why the association technique is also known as relation technique. The association technique is used in market basket analysis to identify a set of products that customers frequently purchase together.
Retailers are using association technique to research customer’s buying habits. Based on historical sale data, retailers might find out that customers always buy crisps when they buy beers, and, therefore, they can put beers and crisps next to each other to save time for the customer and increase sales.
2. Classification
Classification is a classic data mining technique based on machine learning. Basically, classification is used to classify each item in a set of data into one of a predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network, and statistics. In classification, we develop the software that can learn how to classify the data items into groups. For example, we can apply classification in the application that “given all records of employees who left the company, predict who will probably leave the company in a future period.” In this case, we divide the records of employees into two groups named “leave” and “stay”. And then we can ask our data mining software to classify the employees into separate groups.
3. Clustering
Clustering is a data mining technique that makes a meaningful or useful cluster of objects. These objects have similar characteristics using the automatic technique. Furthermore, the clustering technique defines the classes and puts objects in each class. But classification techniques, assignes objects into known classes. To make the concept clearer, we can take book management in the library as an example. In a library, there is a wide range of books on various topics available. The challenge is how to keep those books in a way that readers can take several books on a particular topic without hassle. By using the clustering technique, we can keep books that have some kinds of similarities in one cluster or one shelf and label it with a meaningful name.
4. Regression
In statistical terms, a regression analysis is a process of identifying and analyzing the relationship among variables. it can help you understand the characteristic value of the dependent variable changes if any one of the independent variables is varied. this means one variable is dependent on another, but it is not vice versa.it is generally used for prediction and forecasting.
Data Mining Process and Tools
The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a conceptual tool that exists as a standard approach to data mining. The process outlines six phases:
Business understanding
Data understanding
Data preparation
Modelling
Evaluation
Deployment
The first two phases, business understanding and data understanding, are both preliminary activities. It is important to first define what you would like to know and what questions you would like to answer and then make sure that your data is centralized, reliable, accurate, and complete.
Once you’ve defined what you want to know and gathered your data, it’s time to prepare your data — this is where you can start to use data mining tools. Data mining software can assist in data preparation, modelling, evaluation, and deployment. Data preparation includes activities like joining or reducing data sets, handling missing data, etc.
The modelling phase in data mining is when you use a mathematical algorithm to find a pattern(s) that may be present in the data. This pattern is a model that can be applied to new data. Data mining algorithms, at a high level, fall into two categories — supervised learning algorithms and unsupervised learning algorithms. Supervised learning algorithms require a known output, sometimes called a label or target. Supervised learning algorithms include Naïve Bayes, Decision Tree, Neural Networks, SVMs, Logistic Regression, etc. Unsupervised learning algorithms do not require a predefined set of outputs but rather look for patterns or trends without any label or target. These algorithms include k-Means Clustering, Anomaly Detection, and Association Mining.
Data evaluation is the phase that will tell you how good or bad your model is. Cross-validation and testing for false positives are examples of evaluation techniques available in data mining tools. The deployment phase is the point at which you start using the results.
Importance of Data Mining
1. Marketing / Retail
Data mining helps marketing companies build models based on historical data to predict who will respond to the new marketing campaigns such as direct mail, online marketing campaign…etc. Through the results, marketers will have an appropriate approach to selling profitable products to targeted customers.
Data mining brings a lot of benefits to retail companies in the same way as marketing. Through market basket analysis, a store can have an appropriate production arrangement in a way that customers can buy frequent buying products together with pleasant. In addition, it also helps retail companies offer certain discounts for particular products that will attract more customers.
2. Finance / Banking
Data mining gives financial institutions information about loan information and credit reporting. By building a model from historical customer’s data, the bank, and financial institution can determine good and bad loans. In addition, data mining helps banks detect fraudulent credit card transactions to protect the credit card’s owner.
3. Manufacturing
By applying data mining in operational engineering data, manufacturers can detect faulty equipment and determine optimal control parameters. For example, semiconductor manufacturers have a challenge that even the conditions of manufacturing environments at different wafer production plants are similar, the quality of wafer are a lot the same and some for unknown reasons even has defects. Also, data mining has been applying to determine the ranges of control parameters that lead to the production of the golden wafer.
4. Governments
Data mining helps government agency by digging and analyzing records of the financial transaction to build patterns that can detect money laundering or criminal activities.
Applications of Data Mining
There are approximately 100,000 genes in the human body. Each gene is composed of hundreds of individual nucleotides which are arranged in a particular order. Ways of these nucleotides being ordered and sequenced are infinite to form distinct genes. Data mining technology can be used to analyze the sequential pattern. You can use it to search similarity and to identify particular gene sequences. In the future, data mining technology will play a vital role in the development of new pharmaceuticals. Also, it may provide advances in cancer therapies.
Financial data collected in the banking and financial industry is often relatively complete, reliable, and of high quality. This facilitates systematic data analysis and data mining. Typical cases include classification and clustering of customers for targeted marketing. It can also include detection of money laundering and other financial crimes. Furthermore, we can look into the design and construction of data warehouses for multidimensional data analysis.
The retail industry is a major application area for data mining since it collects huge amounts of data on customer shopping history, consumption, and sales and service records. Data mining on retail is able to identify customer buying habits, to discover customer purchasing pattern and to predict customer consuming trends. This technology helps design effective goods transportation, distribution policies, and less business cost.
Also, data mining in the telecommunication industry can help understand the business involved, identify telecommunication patterns, catch fraudulent activities, make better use of resources and improve service quality. Moreover, the typical cases include multidimensional analysis of telecommunication data, fraudulent pattern analysis and the identification of unusual patterns as well as multidimensional association and sequential pattern analysis.
Summary
The more data you collect…the more value you can deliver. And the more value you can deliver…the more revenue you can generate.
Data mining is what will help you do that. So, if you are sitting on loads of customer data and not doing anything with it…I want to encourage you to make a plan to start diving into it this week. Do it yourself or hire someone else…whatever it takes. Your bottom line will thank you.
Always query yourself how are you bringing value to your business with data mining!
The advancement in the analytical eco-space has reached new heights in the recent past. The emergence of new tools and techniques has certainly made life easier for an analytics professional to play around with the data. Moreover, the massive amounts of data that’s getting generated from diverse sources need huge computational power and storage system for analysis.
Three of the most commonly used terms in analytics are Data mining, Machine Learning, and Data Science which is a combination of both. In this blog post, we would look into each of these three buzzwords along with examples.
Data Mining:
By term ‘mining’ we refer to extracting some object by digging. Similarly, that analogy could be applied to data where information could be extracted by digging into it. Data mining is one of the most used terms these days. Unlike previously, our life is circulated entirely by big data and we have the tools and techniques to handle such voluminous diverse meaningful data.
In the data, there are a lot of patterns which people could discover once the data has been gathered from relevant sources. The hidden patterns could be extracted to provide valuable insights by combining multiple sources of data even if it is junk. This entire process is known as Data mining.
Now the data used for mining could be enterprise data which are restricted and secured and has privacy issues. It could also be an integration of multiple sources which includes financial data, third-party data, etc. The more the data available to us, the better it is as we need to find patterns and insights in sequential and non-sequential data.
The steps involved in data mining are –
Data Collection – This is one of the most important steps in Data mining as getting the correct data is always a challenge in any organization. To find patterns in the data, we need to ensure that the source of the data is accurate and as much as possible data is gathered.
Data Cleaning – A lot of the times the data we get is not clean enough to draw insights from it. There could be missing values, outliers, NULL in the data which needs to be handled either by deletion or by imputation based on its significance to the business.
Data Analysis – Once the data is gathered, and cleaned the next step is to analyze the data which in short known as Exploratory Data Analysis. Several techniques and methodologies are applied in this step to derive relevant insights from the data.
Data Interpretation – Only analyzing the data is worthless unless it is interpreted through the form of graphs or charts to the stakeholders or the business who would make conclusions based on the analysis.
Data mining has several usages in the real world. For example, if we take the logs data for login in a web application, we would see that the data is messy containing information like timestamp, activities of the user, time spent on the website, etc. However, if we clean the data, and then analyze it, we would find some relevant information from it such as the user’s regular habit, the peak time for most of the activities, and so on. All this information could help to increase the efficiency of the system.
Another example of data mining is in crime prevention. Though data mining has most usage in education and healthcare, it is also used by agencies in the crime department to spot patterns in the data. This data would consist of information about some of the criminal activities that have taken place. Hence, mining, and gathering information from the data would help the agencies to predict future crime events and prevent it from occurring. The agencies could mine the data and find out the place where the next crime could take place. They could also prevent cross-border calamity by understanding which vehicle to check, the age of the occupants, etc.
However, a few of the important points one should remember about Data Mining –
Data mining should not be considered as the first solution to any analysis task if other accurate solutions are applicable. It should be used when such solutions fail to provide value.
Sufficient amount of data should be present to draw insights from it.
The problem should be understood to be a Regression or a Classification one.
Machine Learning:
Previously, we learned about Data mining which is about gathering, cleaning, analyzing, and interpreting relevant insights from the data for the business to draw conclusions from it.
If Data mining is about describing a set of events, Machine Learning is about predicting the future events. It is the term coined to define a system which learns from past data to generalize and predict the future events from the unknown set of data.
Machine Learning could be divided into three categories –
Supervised Learning – In supervised learning, the target is labeled i.e., for every corresponding row there is an output value.
Unsupervised Learning – The data set is unlabelled in unsupervised learning i.e., one has to cluster the data into various groups based on the similarities in the pattern of the data points.
Reinforcement Learning – It is a special category of Machine Learning which is mostly used in self-driving cars. In reinforcement learning, the learner is rewarded for every correct move, and penalized for any incorrect move.
The field of Machine Learning is vast, and it requires a blend of statistics, programming, and most importantly data intuition to master it. Supervised and unsupervised learning are used to solve regression, classification, and clustering problems.
In regression problems, the target is numeric i.e., continuous or discrete in nature. A continuous value could be an integer, float, or a decimal, whereas a discrete value is a number or an integer.
In classification problems, the target is categorical i.e., binary, multinomial, or ordinal in nature.
In clustering problems, the dataset is grouped into different clusters based on the similar properties among the data in a particular group.
Machine Learning has a vast usage in various fields such as Banking, Insurance, Healthcare, Manufacturing, Oil and Gas, and so on. Professionals from various disciplines feel the need to predict future outcomes in order to work efficiently and prepare for the best by taking appropriate actions. Some of the real-life examples where Machine Learning has found its usage is –
Email Spam filtering – This is the first application of Machine Learning where an email is classified as ‘Spam’ or ‘Not Spam’ based on certain keywords in the mail. It is a binary classification supervised learning problem where the system is initially trained with a set of sample emails to learn the patterns which would help in filtering out irrelevant emails. Once the system has generalized well, it is passed through a validation set to check for its efficiency, and then through a test set to find its accuracy.
Credit Risk Analytics – Machine Learning has vast influence in the Banking, and Insurance domain with one of its usage being in predicting the delinquency of a loan by a borrower. Defaulting a credit loan is a prevalent issue in which the lender or the bank has lost millions by failing to identify the possibility of a borrower not repaying back the loans or meeting the contractual agreements. However, Machine Learning has been introduced by various banks which takes into several features of a borrower and builds a predictive model which helps in mitigating the risk involved in giving credit card loans to them.
Product Recommendations – Flipkart, and Amazon are of the two biggest e-commerce industry in the world where millions of users shop every day the products of their choice. However, there is a recommendation algorithm that works behind the scenes which simplify the life of the customer by displaying them the products they make like based on their previous shopping or search patterns. This is an example of unsupervised learning where a customer is grouped based on their shopping patterns.
Data Science:
So far, we have learned about the two most common and important terms in Analytics i.e., Data mining and Machine Learning.
If Data mining deals with understanding and finding hidden insights in the data, then Machine Learning is about taking the cleaned data and predicting future outcomes. All of these together form the core of Data Science.
Data Science is a holistic study which involves both Descriptive and Predictive Analytics. A Data Scientist needs to understand and perform exploratory analysis as well as employ tools, and techniques to make predictions from the data.
A Data Scientist role is a mixture of the work done by a Data Analyst, a Machine Learning Engineer, a Deep Learning Engineer, or an AI researcher. Apart from that, a Data Scientist might also be required to build data pipelines which is the work of a Data Engineer. The skill set of a Data Scientist consists of Mathematics, Statistics, Programming, Machine Learning, Big Data, and communication.
Some of the applications of Data Science in the modern world are –
Virtual assistant – Amazon’s Alexa, and Apple’s Siri are two of the biggest achievements in the recent past where AI has been used to build human-like intelligent systems. A virtual assistant could perform most of the tasks that a human being could with proper instructions.
ChatBot – Another common usage of Data Science is the ChatBot development which is now being integrated into almost every corporation. A technique called Natural Language Processing is in the core of ChatBot development.
Identifying cancer cells – Deep Learning has made tremendous progress in the healthcare sector where it is used to identify the pattern in the cells to predict whether it is cancerous or not. Deep Learning uses neural networks which functions like the human brain.
Conclusion
Data mining, Machine Learning, and Data Science is a broad field and it would require quite a few things to learn to master all these skills.
Dimensionless has several resources to get started with.
It’s been said that Data Scientist is the “sexiest job title of the 21st century.” This is because of one main reason that there is a humongous amount of data available as we are producing data at a rate as never before. With the dramatic access to data, there are sophisticated algorithms present such as Decision trees, Random Forests etc. When there is a humongous amount of data available, the most intricate part is to select the correct algorithm to solve the problem. Each model has its own pros and cons and should be selected depending on the type of problem at hand and data available.
Decision Trees:
The aim of this blog post is to discuss one of the most widely used Machine Learning algorithm: “Decision Trees”. As the name suggests, it uses a tree-like model to make decisions as shown in below figure. Decision Tree is drawn upside down with its root at the top. A question is asked at every node based on which decision tree splits into branches. The end of the tree which doesn’t split further is called as Leaf.
Decision Trees can be used for classification as well as regression problems. That’s why there are called as Classification or Regression Trees(CART). In the above example, a decision tree is being used for a classification problem to decide whether a person is fit or unit. The depth of the tree is referred to length of the tree from root node to leaf.
Have you ever given the thought that if there are so many sophisticated algorithms available such as neural networks which are better in terms of parameters such as accuracy then why decision trees are one of the most widely used algorithms?
The biggest advantage of Decision Trees is interpretability. Let’s talk about neural networks to understand this. To make the concept of neural network easy to understand, let’s consider the neural network as “Black Box”. The set of input data is given to the black box and it produces the output corresponding to the input data set. Now, What’s inside the black box? Black Box consists of a computational unit which consists of several hidden layers depending on the intricacy of problem. Also, a large amount of data set is required to train these hidden layers. With the increased no. of hidden layers, there is a significant increase in the complexity of neural networks. It becomes very hard to interpret the output of neural networks in such cases. That’s where lies the importance of decision trees. Decision Trees interpretability helps the humans to understand what’s happening inside the black box? This can help significantly to improve the performance of neural networks in terms of several parameters such as accuracy, avoiding the overfitting etc.
Another advantage of Decision Trees includes a nonlinear relationship between the parameters doesn’t affect the tree performance, Decision implicitly performs the feature selection and minimal effort for data cleaning.
As already discussed, every algorithm has it’s pros and cons. Disadvantages of Decision Trees include poor performance if the decision tree is overfitted to data and could not generalize well. Decision trees can be unstable for small variations of data. So, this variation should be reduced by methods such as bagging, boosting etc.
If you have used ever implemented decision trees: Have you ever thought what’s happening in the background when you implement a decision tree using sci-kit learn in Python? Let’s understand the nitty-gritty of decision trees i.e. various functions such as train test split, checking the purity of data, classification of data, calculating the overall entropy of data etc. that runs in the background. Let’s understand the concept of the decision tree by implementing it from scratch i.e. with the help from numpy and pandas (without using skicit learn).
Here, I am using Titanic dataset to build a decision tree. Decision tree needs to be trained to classify whether the passenger is dead or survived based on parameters such as Age, gender, Pclass. Note that titanic data set contains various variables such as passenger name, address etc which are dropped because they are just identifiers and doesn’t add value to the decision tree. This process is formally called “Feature Selection.”
Data Cleaning:
The first step toward building the ML algorithm is data cleaning. It is one of the most important step because the model build on unformatted data can affect the performance of the model significantly. I am going to use the Titanic data set to build the decision tree. The aim of this model is to classify the passengers as survived or not based on information given. The first step is to load the data set, clean it. Cleaning of data consists of 2 steps:
Dropping the variables which are of least importance in deciding. In Titanic dataset columns such as name, cabin no. ticket no. is of least importance. So, they have been dropped.
Fill in all the missing values i.e. replace NA’s with the most suitable central tendency. All the NA’s are replaced with Mean in case of continuous variables and mode in case of categorical variables. Once the data is clean, we will try to build the helper functions which will help us to write the main algorithm.
Train-Test Split:
We are going to divide the data set into 2 sets i.e. train data set and test data set. I have kept the train test split ratio as 80:20. However, this could be different. The best practice is to keep the test data set small as compared to the train data set depending on the size of the data set. But the test should not be so small that it is not representative of the population
In the case of large data sets, the data set is divided into 3 categories: Training Data Set, Validation Data Set, Test Data Set. Train data is used to train the model, validation data set is used to tune the model in terms of parameters such as accuracy, overfitting. Once the model is verified for the optimal accuracy, it can be used for testing.
Check Data Purity and classify:
As shown in the block diagram, Data Pure function check the data set for its purity i.e. if the data set contains only one species of flower. If so, it will classify the data. If the data sets contain different species of flowers, it will try to ask the questions which can accurately segregate the different species of flowers. It is done by implementing functions such as potential splits, split data, calculate the overall entropy. Let’s understand each of them in detail.
Potential Splits Function:
Potential splits function gives all the possible splits for all of the variables. In Titanic dataset, there are 2 kinds of variables Categorical and Continuous. Both the types of variables are handled in a different way.
For a categorical variable, each unique value is taken as possible split. Let’s take the example of gender. Gender can only take up 2 values either male or female. Possible splits are male and Female. The question will be simple: Is Gender == “Male”. In this way, the decision tree can segregate the population based on Gender.
Since the continuous variable can take on any value. So, the potential split can be exactly in the midpoint of two values in the data set. Let’s understand this with the help of “Fare” Variable in Titanic data set. Suppose, Fare = {10,20,30,40,50}. Potential Splits for Fare will be: 15, 25,35,45. If we ask the question “If Fare <=25”, we can segregate the data effectively. Since Fare variable can take up any value between 10 to 50 in the above case, we can’t deal the continuous variable in the same way as a categorical variable.
Once potential split function gives all the potential splits for all the variables. Split data function is used to split data based on each and every potential split. It divides the data into 2 halves, data above and data below. Entropy is calculated for each split.
Measure of Impurity:
There are two methods to measure the impurity: Gini Impurity and Information Gain Entropy. Both the methods work the same and the selection of impurity measure has little impact on the performance of the decision tree. But Entropy is computationally expensive since it deals with the logarithmic functions. This is the main reason for the large-scale use of Gini Impurity over Information Gain Entropy. Below are formulae for both:
Gini: Gini(E)= 1-∑j=1(pj2)
Entropy: H(E)=−∑cj=1(pjlogpj)
I have used Information Gain Entropy as a measure of impurity. You can use either of them, as both give pretty much same results specifically in case of CART analytics as shown in below figure. Entropy in simpler terms is the measure of randomness or uncertainty. For each potential split, entropy will be calculated. The best potential split will be the one with the lowest overall entropy. This is because of lower the entropy, lower the uncertainty and hence more the probability.
Refer to below link for the comparison between the two methods of impurity measure:
To calculate entropy in titanic dataset example, calculate entropy and calculate overall entropy functions are used. The functions are defined based on the equations explained above. Once the data is split into two halves by split data function, entropy is calculated for each and every potential split. The potential split with lowest overall entropy is selected as best split with the help of determining the best split function
Determine type of feature:
Determine type of feature functions determine whether a type of feature is categorical or continuous. There are 2 criteria’s for the feature to be called as categorical, first if the feature is of data type string and second, the no. of categories for the feature is less than 10. Otherwise, the feature is said to be continuous.
Determine type of feature determines whether the function is categorical or not based on the above criteria which act as input to a potential split function. This is because potential split has a different way to handle categorical and continuous data as discussed in the potential split function above
Decision Tree Algorithm:
Since we have built all the helper functions, it’s now time to build the decision tree algorithm.
Target Decision Tree should look like:
As shown in the above diagram, Decision Tree consists of several sub-trees. Each sub-tree is a dictionary where “Question” is key of the dictionary and there are two answers corresponding to each question i.e. Yes answer and No answer.
Decision Tree Algorithm is the main algorithm which is used to train the model with the help of helper functions which we built previously. Firstly, Train Test Split function is called which divides the data set into train data and test data. Once the data is split, Data Pure and Classify function is called to check the purity of data and classify the data based on purity. The potential Split function gives all the potential splits for all variables. Overall Entropy is calculated for each potential split and eventually, potential split with lowest overall entropy is selected and split data function splits the data into two parts. In this way, the subtree is built and a series of sub-trees constitutes our target Decision Tree.
Once the model is trained on training dataset, the performance of Decision Tree is verified on the test data set with the help of classifying function. The performance of the model is measured in terms of accuracy by calculating accuracy function. The performance of the model can be improved by pruning the tree based on the max depth of the tree. The accuracy of the model is coming out to be 77%.
Never thought that online trading could be so helpful because of so many scammers online until I met Miss Judith... Philpot who changed my life and that of my family. I invested $1000 and got $7,000 Within a week. she is an expert and also proven to be trustworthy and reliable. Contact her via: Whatsapp: +17327126738 Email:judithphilpot220@gmail.comread more
A very big thank you to you all sharing her good work as an expert in crypto and forex trade option. Thanks for... everything you have done for me, I trusted her and she delivered as promised. Investing $500 and got a profit of $5,500 in 7 working days, with her great skill in mining and trading in my wallet.
judith Philpot company line:... WhatsApp:+17327126738 Email:Judithphilpot220@gmail.comread more
Faculty knowledge is good but they didn't cover most of the topics which was mentioned in curriculum during online... session. Instead they provided recorded session for those.read more
Dimensionless is great place for you to begin exploring Data science under the guidance of experts. Both Himanshu and... Kushagra sir are excellent teachers as well as mentors,always available to help students and so are the HR and the faulty.Apart from the class timings as well, they have always made time to help and coach with any queries.I thank Dimensionless for helping me get a good starting point in Data science.read more
My experience with the data science course at Dimensionless has been extremely positive. The course was effectively... structured . The instructors were passionate and attentive to all students at every live sessions. I could balance the missed live sessions with recorded ones. I have greatly enjoyed the class and would highly recommend it to my friends and peers.
Special thanks to the entire team for all the personal attention they provide to query of each and every student.read more
It has been a great experience with Dimensionless . Especially from the support team , once you get enrolled , you... don't need to worry about anything , they keep updating each and everything. Teaching staffs are very supportive , even you don't know any thing you can ask without any hesitation and they are always ready to guide . Definitely it is a very good place to boost careerread more
The training experience has been really good! Specially the support after training!! HR team is really good. They keep... you posted on all the openings regularly since the time you join the course!! Overall a good experience!!read more
Dimensionless is the place where you can become a hero from zero in Data Science Field. I really would recommend to all... my fellow mates. The timings are proper, the teaching is awsome,the teachers are well my mentors now. All inclusive I would say that Kush Sir, Himanshu sir and Pranali Mam are the real backbones of Data Science Course who could teach you so well that even a person from non- Math background can learn it. The course material is the bonus of this course and also you will be getting the recordings of every session. I learnt a lot about data science and Now I find it easy because of these wonderful faculty who taught me. Also you will get the good placement assistance as well as resume bulding guidance from Venu Mam. I am glad that I joined dimensionless and also looking forward to start my journey in data science field. I want to thank Dimensionless because of their hard work and Presence it made it easy for me to restart my career. Thank you so much to all the Teachers in Dimensionless !read more
Dimensionless has great teaching staff they not only cover each and every topic but makes sure that every student gets... the topic crystal clear. They never hesitate to repeat same topic and if someone is still confused on it then special doubt clearing sessions are organised. HR is constantly busy sending us new openings in multiple companies from fresher to Experienced. I would really thank all the dimensionless team for showing such support and consistency in every thing.read more
I had great learning experience with Dimensionless. I am suggesting Dimensionless because of its great mentors... specially Kushagra and Himanshu. they don't move to next topic without clearing the concept.read more
My experience with Dimensionless has been very good. All the topics are very well taught and in-depth concepts are... covered. The best thing is that you can resolve your doubts quickly as its a live one on one teaching. The trainers are very friendly and make sure everyone's doubts are cleared. In fact, they have always happily helped me with my issues even though my course is completed.read more
I would highly recommend dimensionless as course design & coaches start from basics and provide you with a real-life... case study. Most important is efforts by all trainers to resolve every doubts and support helps make difficult topics easy..read more
Dimensionless is great platform to kick start your Data Science Studies. Even if you are not having programming skills... you will able to learn all the required skills in this class.All the faculties are well experienced which helped me alot. I would like to thanks Himanshu, Pranali , Kush for your great support. Thanks to Venu as well for sharing videos on timely basis...😊
I highly recommend dimensionless for data science training and I have also been completed my training in data science... with dimensionless. Dimensionless trainer have very good, highly skilled and excellent approach. I will convey all the best for their good work. Regards Avneetread more
After a thinking a lot finally I joined here in Dimensionless for DataScience course. The instructors are experienced &... friendly in nature. They listen patiently & care for each & every students's doubts & clarify those with day-to-day life examples. The course contents are good & the presentation skills are commendable. From a student's perspective they do not leave any concept untouched. The step by step approach of presenting is making a difficult concept easier. Both Himanshu & Kush are masters of presenting tough concepts as easy as possible. I would like to thank all instructors: Himanshu, Kush & Pranali.read more
When I start thinking about to learn Data Science, I was trying to find a course which can me a solid understanding of... Statistics and the Math behind ML algorithms. Then I have come across Dimensionless, I had a demo and went through all my Q&A, course curriculum and it has given me enough confidence to get started. I have been taught statistics by Kush and ML from Himanshu, I can confidently say the kind of stuff they deliver is In depth and with ease of understanding!read more
If you love playing with data & looking for a career change in Data science field ,then Dimensionless is the best... platform . It was a wonderful learning experience at dimensionless. The course contents are very well structured which covers from very basics to hardcore . Sessions are very interactive & every doubts were taken care of. Both the instructors Himanshu & kushagra are highly skilled, experienced,very patient & tries to explain the underlying concept in depth with n number of examples. Solving a number of case studies from different domains provides hands-on experience & will boost your confidence. Last but not the least HR staff (Venu) is very supportive & also helps in building your CV according to prior experience and industry requirements. I would love to be back here whenever i need any training in Data science further.read more
It was great learning experience with statistical machine learning using R and python. I had taken courses from... Coursera in past but attention to details on each concept along with hands on during live meeting no one can beat the dimensionless team.read more
I would say power packed content on Data Science through R and Python. If you aspire to indulge in these newer... technologies, you have come at right place. The faculties have real life industry experience, IIT grads, uses new technologies to give you classroom like experience. The whole team is highly motivated and they go extra mile to make your journey easier. I’m glad that I was introduced to this team one of my friends and I further highly recommend to all the aspiring Data Scientists.read more
It was an awesome experience while learning data science and machine learning concepts from dimensionless. The course... contents are very good and covers all the requirements for a data science course. Both the trainers Himanshu and Kushagra are excellent and pays personal attention to everyone in the session. thanks alot !!read more
Had a great experience with dimensionless.!! I attended the Data science with R course, and to my finding this... course is very well structured and covers all concepts and theories that form the base to step into a data science career. Infact better than most of the MOOCs. Excellent and dedicated faculties to guide you through the course and answer all your queries, and providing individual attention as much as possible.(which is really good). Also weekly assignments and its discussion helps a lot in understanding the concepts. Overall a great place to seek guidance and embark your journey towards data science.read more
Excellent study material and tutorials. The tutors knowledge of subjects are exceptional. The most effective part... of curriculum was impressive teaching style especially that of Himanshu. I would like to extend my thanks to Venu, who is very responsible in her jobread more
It was a very good experience learning Data Science with Dimensionless. The classes were very interactive and every... query/doubts of students were taken care of. Course structure had been framed in a very structured manner. Both the trainers possess in-depth knowledge of data science dimain with excellent teaching skills. The case studies given are from different domains so that we get all round exposure to use analytics in various fields. One of the best thing was other support(HR) staff available 24/7 to listen and help.I recommend data Science course from Dimensionless.read more
I was a part of 'Data Science using R' course. Overall experience was great and concepts of Machine Learning with R... were covered beautifully. The style of teaching of Himanshu and Kush was quite good and all topics were generally explained by giving some real world examples. The assignments and case studies were challenging and will give you exposure to the type of projects that Analytics companies actually work upon. Overall experience has been great and I would like to thank the entire Dimensionless team for helping me throughout this course. Best wishes for the future.read more
It was a great experience leaning data Science with Dimensionless .Online and interactive classes makes it easy to... learn inspite of busy schedule. Faculty were truly remarkable and support services to adhere queries and concerns were also very quick. Himanshu and Kush have tremendous knowledge of data science and have excellent teaching skills and are problem solving..Help in interviews preparations and Resume building...Overall a great learning platform. HR is excellent and very interactive. Everytime available over phone call, whatsapp, mails... Shares lots of job opportunities on the daily bases... guidance on resume building, interviews, jobs, companies!!!! They are just excellent!!!!! I would recommend everyone to learn Data science from Dimensionless only 😊read more
Being a part of IT industry for nearly 10 years, I have come across many trainings, organized internally or externally,... but I never had the trainers like Dimensionless has provided. Their pure dedication and diligence really hard to find. The kind of knowledge they possess is imperative. Sometimes trainers do have knowledge but they lack in explaining them. Dimensionless Trainers can give you ‘N’ number of examples to explain each and every small topic, which shows their amazing teaching skills and In-Depth knowledge of the subject. Himanshu and Kush provides you the personal touch whenever you need. They always listen to your problems and try to resolve them devotionally.
I am glad to be a part of Dimensionless and will always come back whenever I need any specific training in Data Science. I recommend this to everyone who is looking for Data Science career as an alternative.
All the best guys, wish you all the success!!read more
