Top 11 SQL queries that a data scientist should know

Top 11 SQL queries that a data scientist should know

A relational database management system uses SQL for managing data and for storing it in tabular form. SQL stands for Structured Query Language and it is a query language that is extremely useful for data scientists.

best data analytics course

If you want to learn data analytics and get a data science certification course done for your progressive career, SQL is absolutely essential.

Here are some essential SQL queries for data scientists. Data scientists need to be able to retrieve and store the data which you have derived without the help of others.

Must know SQL queries for a data scientist

A data scientist must know the following basic queries to store and manage data in a tabular form in SQL. These commands may be enumerated as follows:

For creating databases

Before creating a database you first need to check what are the databases that are already present. For that, the command is:

mysql> SHOW DATABASES;

Fresh databases can be created with the understated command:

mysql> CREATE DATABASE medium;

For creating tables

Tables can be created in databases with the following command. Here is an example of creating a table for students: 

mysql> CREATE TABLE (Student [table “” not found /]
) (

    -> Id int primary key,

    -> Name varchar(20),

    -> Subject varchar(20),

    -> registered char(1)

    -> );

The primary key establishes that each row is uniquely identified.

Delete a table

For deleting a database, you will have to use this command:

mysql> DROP DATABASE medium;

Add new columns

New columns can be added using the ‘alter table’ statement and using the following command:

mysql> ALTER TABLE student ADD gpa DECIMAL(3,2);

Delete a column

A column can be deleted using the ‘alter table’ statement and using the following command:

mysql> ALTER TABLE student DROP gpa;

Describe statement

if you want to ascertain a specific table’s overview, then the following command will be used:

mysql> DESCRIBE student;

Add a new row

After the creation of the table, you need to add rows and values to it otherwise it will remain empty. Rows can be added to tables by using the following command:

mysql> INSERT INTO student VALUES(1, “mary”, “accounts”, “F”);

mysql> INSERT INTO student VALUES(2, “Anderson”, “Mathematics”, “F”);

mysql> INSERT INTO student VALUES(3, “George”, “Physics”, “S”);

mysql> INSERT INTO student VALUES(4, “Natalie”, “Biology”, “S”);

The values have to be in accordance with the predefined data type.

Delete a row

If you are trying to delete a row then the ‘delete from’ command has to be used in the following way:

mysql> DELETE FROM student (table name);

WHERE (condition);

The condition for deleting the row must be declared after the ‘where statement’.

Update a row

If any update or addition is required in the rows that already exist, that can also be made in the tables. This command must be used for the same:

mysql> UPDATE student SET subject= “Accounts” WHERE Id = 2;

The update must be specified after the keyword ‘set’ and the condition has to be specified after the keyword ‘where’.

Select a row or rows

Queries are run so that data can be retrieved from databases and the ‘select statement’ is used for that purpose. You can specify the row or the column that you need to retrieve data from, using the ‘select command’ in the following manner:

mysql> SELECT Name, Subject FROM student (table name);

Select based on a condition

To retrieve the data from all the rows while applying some filters and conditions to the rows to make the data analysis more precise, you should use  the following command needs to be used:

mysql> SELECT * FROM student WHERE Subject = “Mathematics”;

Conclusion

Learn data analytics to get a hang of the most important SQL queries to become a data scientist. Enrol in the Data Analytics course with placement by Imarticus and it will act as a launchpad for your career.

Building a data analytics project with Python? Here are a few tips

Building a data analytics project with Python? Here are a few tips

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We are in a technology-driven era where everything is based on data and the reports or insights derived from data. The scope for a data analyst is increasing day by day and if you want to learn data analytics and gain the foundation knowledge of data science for business application then we’re here to provide you with the first step towards your goal. You will get your desired data analytics course with placement.

What is data analytics?

Data analytics is the procedure of thoroughly going through the data and examining it in groups and sets. It is done to find the trends and draw certain conclusions based on the information they have. With more technological advancements, Data Analytics is majorly done through various software, systems and techniques.

For the most part, Data Analytics refers to an array of applications that works through fundamental business intelligence, online analytical processing and reporting. Hence, the use and importance of Data Analytics in business can never be understated.

Data Analytics and python

As data analysis is done for finding out the future course of action in any organisation or business, it also encompasses various programming languages. The use of various programming languages helps organisations to make decisions more precisely and efficiently. 

The use of python in data science and analytics is not unknown. Data scientists use various programming languages including python to prepare data for statistical analysis. Python is also used in other fields as well rather than only Data Analytics which also proves to be very effective for any organisation.

Python is used for general purposes and provides three major advantages which have been enumerated as follows:

  • More readable when compared to other programming languages.
  • Simpler to work with and compile data.
  • More flexible in the learning phase of the language and easy to understand.

Uses of python in Data Analytics

Python has become one of the most popular and widely used programming languages in the world in recent years. Its uses range from machine learning to building web pages and also to software testing. Python is a general-purpose language and its usage is wide enough to cover data science, software and web development, automation, etc. 

Python can be used for the following purposes in an organisation:

  • Developing web pages and software.
  • Automation on scripting.
  • Data analysis and data visualisation.
  • Software testing and web development.

The main focus of the scores is python’s usage and data analysis and machine learning. Python has become a basic requirement for data analysts and scientists to conduct complex statistical calculations in data science. It also has to create data visualisation, create machine learning algorithms, analyse and compare data, etc.

Data analysts and data scientists use the graphic visualisations that python builds. The visualisations include graphs, charts, histograms, and flowcharts that give an official representation of the data that has been assessed. Based on that data the future course of action for the organisation will be taken by the authorities.

Python also has a lot of libraries that can help programmers code programs for data analysis more quickly and efficiently. It helps the data to become more concise and precise.

Essential Tips

Here are some essential tips for building a data analytics project with Python:

  • Use a python cheat sheet for syntax.
  • Clean and rid your data of noise.
  • Use Python’s interface and the available plugins to prepare the data without the need to write too much code.
  • Use an online compiler for projects such as Google Collab that can run your project in real time.
  • Use libraries such as pandas, NumPy, Tensorflow, PyTorch, SciPy and scikit-learn.
  • Use Matplotlib for visualisations.
  • Format and structure your target data for efficient analysis.
  • Use statistical methods for handling and processing your data.
  • Use nested list comprehension to get rid of for loop complexities. 

Conclusion

Data analytics is a great discipline to be a part of and to have a successful career. Previously data was collected from a single source in a standard format but now as data science has become more advanced, data is collected from multiple sources and the requirement of data scientists and data analysts is increasing day by day. Learn Python online training course with Imarticus and kickstart your career towards growth. We offer courses with placements for your bright future.

You want to learn artificial intelligence: What next?

You want to learn artificial intelligence: What next?

Artificial intelligence is an evolving technology that has been providing us with a plethora of job opportunities. With the advancement of data science and technology, artificial intelligence has made a mark on the lives of people. Not only locally, AI and machine learning also have a huge impact on the market. If you are looking for a course to learn python online, or learn artificial intelligence online, keep reading to know about this certificate course provided by IIT Guwahati and excel in your skills. 

AI or Artificial Intelligence is a technology that helps the machine to simulate human behaviour accurately. Artificial intelligence is a very trending technology in the course of data science which is the future for creating an efficient intelligent system. Sometimes, amateurs confuse AI and machine learning to be of the same category, however, it is not the same as one another. Machine learning, on the other hand, is a subset of AI which helps a machine learn past data that has been processed into it without being explicitly programmed to it.

AI and machine learning however do have the common goal to simplify complex problems to be solved much more efficiently. If you have pursued a career in data science and want to excel in your further knowledge in AI, this Machine learning certificate course by IIT Guwahati will be beneficial for you. You can learn about the principles of AI and machine learning with industry approved curriculum. 

Certification In Artificial Intelligence And Machine Learning At E&ICT Academy, IIT Guwahati 

best data analytics certification course

IIT Guwahati has been offering this lucrative course to learn artificial intelligence online which will help to excel in the career of the learner in the field. IIT Guwahati has been offering a plethora of course materials based on this subject which will help you excel in your knowledge of AI and machine learning.

The faculty who will be taking the classes for this course are highly trained and will provide the learners with their keen insights. Learn about the prerequisites, job opportunities and other important notions that will benefit you in taking up this career. 

Prerequisites 

If you are interested in pursuing this certificate course on artificial intelligence and machine learning, then you should have a bachelor’s or a master’s degree in either of the following disciplines. They are: 

  • Computer Science 
  • Engineering 
  • Maths 
  • Statistics 
  • Economics 
  • Any other science stream subject. 

The learner should have a minimum of 50% in aggregate in graduation. 

Job Opportunities 

With the advancement of the disciplines in data science, AI and machine learning is increasing by leaps and bounds. This method has been adopted by several multinational corporations and there are swathes of inspiration for learners who are thinking of taking up this course. The base salary of a Data scientist in India is an average of Rs.881,097 in a year. The job opportunities after completion of this course are feasible and will perk up career opportunities. Besides, the course has given job opportunities to many learners in Deloitte, Infosys, KPMG, Accenture, Kotak Mahindra Bank etc. 

Assignments And Mode Of Training

Learners have to do online training classes for getting this certificate course. They have to dedicate 8hours a week to attending live sessions by academicians and industry experts. There will be more than 100+ assignments alongside 25 real industry projects which will help in providing practical knowledge to know about JupyterHub, NumPy, OpenCV etc. 

Conclusion 

AI and Machine learning has been providing a plethora of learning opportunities to individuals which helps in advancing careers for individuals in data science. You should check out Imarticus’s website about pursuing the Certification Of Artificial Intelligence And Machine Learning which will give you a keen knowledge of data science and its different technological subsets. 

The domains of AI and Machine Learning (ML) are one of the fastest-growing sectors in the world. We are increasingly getting more dependent on services and products that are driven by AI and ML. AI/ML makes our systems faster, making our jobs easier. In this domain, there are always innovations being introduced and researched. Thus, the AI/ML sector is an extremely lucrative sector to join. 

I chose the wrong data science course and how you can learn from my mistake

I chose the wrong data science course and how you can learn from my mistake

The field of Data Science is ever-growing. People from all walks of the IT sector are getting trained in data science as it seems to be one of the most promising career paths of the future, If you know your way around the field, then you know that data science is exactly that, a science. Not just a part of IT and business. A lot of people are opting for data science online training to build a solid career in data science. But what if I told you, that one of the biggest mistakes you can make is choosing the wrong data science online training course and wasting your precious time? Not only that but there are a lot of mistakes you can make while self-studying for data science. This article can help you refrain from making these mistakes and tell you which path will take you to the top fast. 

Some Common Mistakes People Make while Learning Data Science 

Now that we have delved into the topic, let us talk about some of the most common mistakes people make out of misconception while learning data science. 

Choosing the Wrong Course

online learning

You will have a lot of choices to choose from if you want to learn data science. There are many courses, books, videos and blogs at your fingertips. But overwhelming yourself with options is not the right way to go. Instead, focus on the courses that match your learning style and choose books and blogs that seem easy to you.

You have to slowly level up in this field and it can take a long time. One of the courses that can help you in this field is the IHUB Data Science and Machine Learning certification course. Which is an IIT Data Science Certification Course to help people get started on their career path as data scientists. It is a comprehensive course that teaches you the basics of data science and machine learning. 

Lack of Projects 

Learning data science includes a lot of studying, but it also involves a lot of hands-on projects. A lot of the time, people get so immersed in studying that they forget that in order to have a good career in data science, they will also need practical experience in the field. The most important thing to do alongside studying is making sure you work on a project for each thing you learn. That way, you will have real-life experience of what you are studying. 

Lack of Time Management and Planning 

One of the mistakes aspiring data scientists make while learning the subject is underestimating how much time and commitment it takes to properly learn data science. Another one is lacking a solid plan. Depending on your study methods and what you want to learn, you have to have a future plan for your study course. It should include all the courses, books, topics and projects you want to take on. This way, you will understand the subject better and also manage your time well. 

Too Many Research Papers 

Last but not the least, make sure you do not get too lost in research papers associated with your topics. It is normal to want to expand your horizons and learn about what the experts have to say about the topic you are pursuing. But often these high-end research papers can be too hard to comprehend for new learners. Better keep these for when you have a better understanding and grasp of the subject. 

Conclusion 

Now that you have learned which mistakes to avoid while learning data science, you can go about your way to pursue the subject. Remember that you can always learn from your mistakes down the line and make yourself better in the future. Data Science is a very specialised domain and you must be extra careful to avoid the mistakes that have been discussed. Making these mistakes can lead to you wasting your precious time, money and other resources. 

For instance, if you do not take up real-world projects and simply keep learning from study material or lessons, you will lack the experience to be job-ready. Companies are also more likely to hire you if you have deployed projects rather than how many courses you have completed. However, a well-rounded certification such as the one offered by Imarticus can help out a lot. 

How to learn natural language processing if you’re bad at math

You have nothing to fear if you believe your math skills are lacking. You must be familiar with math principles to use natural language processing. Even if studying arithmetic is challenging, using the appropriate approach will be helpful. You only need to know math fundamentals to learn about natural language processing. To groom your math fundamentals in data science, you have multiple e-learning platforms that offer you some of the best data science courses with new-age curriculum and syllabus.

The most crucial aspect is to have a curious mindset and a learning attitude. This will help you identify the simple methods to solve the technical issues. You don’t have to worry if you are not a professional in math since learning NLP requires basic math such as Linear Algebra, Calculus, probability, and statistics.

Natural language processing is the branch of Computer Science that deals with Artificial Intelligence. Here, the computer is given the ability to understand the text and spoken language of human beings. Rule-based modeling, statistical learning, and deep learning models are used in Natural language Processing. These models are essential for the computer to comprehend spoken language.

Since machine learning uses data to learn the mathematical relationship between input and output, it is necessary to know basic math. In NLP, math is concerned with simply calculating demographic data and customer group identification. It deals with predictions and classification.

Hunt the Textbooks

Some of the courses for learning Natural Language Processing offers insightful resource material. It comprises the empirical path of finding out trial and error. You can practice several practice papers and master natural Language Processing without worrying about arithmetic. You may keep track of your progress by testing the codes on the software packages listed in the resource material. You can use the trial and error method rather than concentrating on complex ideas like “Boolean Logic.”

Online

Do It Yourself Tutorial– 

You can enroll in the course and use the tutorial lessons to try your hand at NLP. You can follow the simple instructions in these Scikit-Learn, R, and WEKA tutorials. This will enable you to try them out and aid your coding knowledge. Various levels exist, including Beginner, Intermediate, and Professional. You can test all kinds of challenges with the aid of these levels. Apart from that, multiple algorithms will simplify the mathematical equations. Hence, enabling you to learn Natural Language Processing. These online tools help you solve easy to complex math problems since the tool has the formula. 

Exploring libraries– 

The NLP learning courses offer a vast arena to explore libraries. There are multiple books and online study material for you to browse. Apart from this, the libraries also consist of projects on a trial basis. Thus, enabling you to try multiple demo projects. 

Text Pre-processing

The text pre-processing doesn’t require you to be an expert in using it. You can begin learning the fundamentals of NLP with your current level of arithmetic proficiency. The first stage in learning natural language processing may be text processing. The text umbrella approach is used in text processing to prepare the text for additional analysis. Along with the Lexicon, this also includes stemming, lemmatization, tokenization, and stopword elimination. Here,the pre-processing methods are straightforward and accessible. 

Keyword Extraction

NLP involves analysing the text and extracting the relevant words. It is the process of extracting keywords and analysing the text. It is an automated process of extracting helpful text by using Artificial Intelligence. The software allows you to extract keywords from your specified category and is pretty customisable. For instance, the automatic keyword extractor works if you want to remove the names. Your ability to guess the names and other pronouns is decreased.

Investigation of Algorithms

The NLP courses focus on investigating pre-existing algorithms. Here, the machine learning online training structure provides projects on which the algorithms have already been applied. Choose the correct algorithm and comprehend the application procedure. This will also aid in your comprehension of how the algorithm was used to generate the desired result. As a result, you can practice the algorithms without dealing with challenging mathematical problems.

Although essential, mathematics is not too complicated for you to understand. You can study NLP with the necessary course materials and carefully selected lesson videos. You can learn logical and abstract NLP concepts by enrolling in the online machine learning class.

For more information about Natural Language Processing, you can contact us by using the link on the webpage.

You can browse our data science course and training facilities in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad. You can also contact us using the live chat support, available 24/7.

Frequently Asked Questions

Which topics are included in the data science course?

A beginner’s Data Science course syllabus encompasses fundamental and advanced principles in data analytics, machine learning, statistics, and programming languages like Python or R. The curriculum guides students in interpreting large datasets, recognising patterns, and constructing predictive models. Additionally, the course delves into aspects of data security, ensuring a comprehensive understanding of safeguarding information in Data Science.

Can a fresher apply for a data science course?

Data Science is rapidly becoming a highly sought-after career choice, particularly for fresh graduates with a Computer Science, Mathematics, and Statistics foundation. The job landscape in India is poised for significant growth, with projections indicating the creation of 11 million Data Science jobs by 2026. This underscores the increasing demand and promising opportunities in the field for aspiring professionals.

Who can be eligible for a data science course?

Typically, individuals with science, technology, mathematics, or engineering backgrounds are eligible to enrol in a data science course. The eligibility criteria often include a graduation in Science or Engineering with a minimum aggregate of 50%, where subjects like Mathematics, Statistics, Computer Science, or Information Technology are considered core requirements.

Why learning inventory management is important for a supply chain analyst

Why learning inventory management is important for a supply chain analyst

Any company’s inventory consists of a complete list of items like the stock of goods, property, or contents. It is often considered the most vital asset for that company. And inventory management is where all aspects of the supply chain system merge; therefore, a thorough understanding of inventory management is a critical requirement for a supply chain analyst.

A deficit of inventory during needed times might lead to dissatisfaction among customers. On the other hand, a surplus of stock would also cause issues. Proper care must be taken to prevent spoilage, theft, or damage, adequate insuring, etc.

Large companies with highly complex supply chains ought to strike the right balance between keeping too much and too little inventory. One can achieve this through detailed analysis, learning, and inventory tracking by the supply chain analyst.

What is an inventory management system?

The entire process of ordering, storing, tracking, and using inventory throughout the supply chain is called an inventory management system. While previously only very rudimentary methods for inventory management were used, such as spreadsheets, manual order placements, and hand-counted stock records, now it has been replaced by advanced tracking software.

It is also certain that in the future, with rapid innovations in the field of Artificial Intelligence, the Internet of Things, blockchain, and quantum computing, supply chain operators will continue to transform the inventory management system drastically.

How does inventory management function?

The primary goal of inventory management is to ensure that the right product is kept in the right place and delivered at the right time. This entire inventory tracking, from manufacturer to warehouses and then the sale, must take place efficiently. For that to happen, inventory management has to abide by three fundamental steps.

1. Purchasing inventory: ready-to-sell goods are directly transferred for storage in the warehouse or given up for sale.

2. Storing inventory: goods and materials are stored until required. At a later point, they are transferred into the fulfillment process and made ready to sell.

3. Profiting from inventory: the amount of output available for sale is regulated, and finished goods are held in the inventory for a period, which increases the value and thereby adds to inventory profit.

Inventory management for supply chain professionals

Effective inventory management can significantly facilitate the flow of goods and services between businesses to their respective customers- the primary responsibility of supply chain professionals. Supply change management also includes the warehousing and movement of raw materials, work-in-process inventory, tracking of finished goods, and order fulfillment from the point of origination to the final sale. Below is a list of features of inventory management that greatly helps supply chain analysts and professionals achieve their goals:

· Inventory tracking: knowing exactly where a good or product is located enables supply chain professionals to get hold of it and transfer it forward in the supply network.

· Order management: tracking orders, customizing pricing, initiating the return and subsequent refund, and sending quotes all ease the transaction between customers and the business.

· Transfer management: since the essence of supply chain management is enabling inventory movement and storage, transferring product to where it is most valued is an indispensable quality in the supply chain relationship.

· Reporting and analytics: inventory management’s reporting and analytics aspects permit supply chain analysts to evaluate and predict future sales- this would significantly reduce the chances of unprecedented shortages or surplus in goods.

· Purchasing: creating, managing, and tracking purchase and orders simplify the supply chain network.

· Shipping capabilities: automating shipping of goods from the inventory would eliminate the risks of late deliveries or delivering wrong packages.

A career in supply chain management

The purview of supply management is vast- it encompasses aspects from manufacturing, packaging, storing, transporting, logistics, and final delivery- therefore, it creates multiple job positions from different sectors. Some of the most notable careers in supply chain management are production planning, Logistics Resource planning, Transport administration, Warehouse management, Quality management, Purchasing management, etc.

The diverse sectors incorporated in supply chain management offer numerous job roles and positions for employment-seekers. As India is experiencing a surge in industrial companies and markets, the demand for supply chain professionals also mounts. Moreover, as you enter the supply chain industry, you will have to harbor a wide array of transferable skills- planning, management and administration, evaluation and forecasting, workflow optimization, etc.- that would be useful for any career path.

Young job-seekers can capitalize on this opportunity offered in the supply chain management domain by enrolling in the Professional Certification in Supply Chain Management & Analytics course of IMARTICUS Learning. It is an online course developed by IIM Roorkee that prepares you for the high-demand job roles in supply chain management.

To avail of this course in Professional Certification in Supply Chain Management & Analytics, you can contact us through chat support. You can also drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Hyderabad, Kolkata, Patna, Cochin, Jaipur, Dehradun, Lucknow, or Ahmedabad.

Can credit analysts be replaced by machines? Here’s what you need to know

Can credit analysts be replaced by machines? Here’s what you need to know

With the extensive usage of machines, and technologies like artificial intelligence (AI), most routine tasks are now automated with reduced involvement of humans. Most of the functions that machines handle give better efficiency, more accuracy, and higher productivity, be it performing credit analysis. Such reasons will eventually put a question mark on professions like credit underwriter jobs.

According to the famous media house Business Standard, most jobs will be automated in the next 15-20 years. This conveys that people like us may lose jobs and be replaced by machines. This may drastically affect individuals’ financial growth.

Since machines will be working everywhere, you may ask, will credit underwriter jobs be at risk too? Can a credit analyst course or a certification in credit risk management be helpful? Let’s find out.

What Are Credit Underwriter Jobs?

First, you should know that a credit analyst is a professional who accesses the creditworthiness of individuals to help them perform their financial obligations successfully.

Their job is to do massive financial calculations and analyses of the creditworthiness of companies and securities. They gather past financial information from the customer and then evaluate their financial health and ability to repay the credit.

Their primary role is to provide the best solution to repay credit and improve the economic growth of individuals, companies, and even securities. Their main aim is to lower the financial risk and find the best possible solution within the limitations of the customers.

This Is What We Think Will Be Helpful

Do you know that the demand for automation occurs when the human efficiency and productivity of work decrease? In such cases, inefficient human resources get replaced by machines. However, machines lack creative thinking ability and qualitative and critical analysis.

However, humans have better creative thinking and qualitative and essential analysis abilities. Moreover, they have common sense while performing tasks. So, people will prosper in this field if they gather the skill set required for credit underwriting jobs.

Since a credit analyst not just analyzes credit worthiness, it also provides solutions according to the situation and its limitations. A machine will give the answers based on the data stored in them. They can’t refine new solutions according to the problem and the risks involved.

The following are steps that will help you ace credit analyst jobs:

·         Develop the required skills for the credit analyst role

·         Do courses and build knowledge and skills for this role

·         Provide better understanding and solutions to the problems

·         Perform certifications: this will prove your worthiness for this job role

Therefore, as a credit analyst, you will not just assess the situation correctly but also provide the best solution for your financial problem. Thus, it’s time to step up and gather the resources to secure the credit analyst role.

Time For Action

Machines won’t replace credit analysts. Instead, they will work together to improve efficiency, speed, reliability, and accuracy. Thus, It’s time for you to quickly take action and develop skills to improve productivity and provide the best solutions to your customers.

Take Up Credit Analyst-Related Courses

Look out for a credit analyst course or a certification in credit risk management. Having more accomplishments will upgrade your chances for such functions. Moreover, these credentials add to your skill set and help shape your career in this domain. These are the introductory courses whose accords are in demand in the financial market. Some of them are even rewarded.

Develop Skills by Getting Industry Exposure

Start working at an early stage. You can ask your tutors to help you get a little more practical exposure. Take up projects and look for work. You can always start with internships when you want to learn.

Suppose you are interested in finance and accounting and are willing to serve companies and consumers to solve their financial issues. Then credit underwriter jobs are perfect for you. There are various opportunities out there for the credit analyst role. Investment companies, commercial and private banks, and credit card companies are looking for an efficient individual with the proper skill set for credit underwriter jobs.

Bottom Line

Soon, technology will inevitably automate specific jobs. However, people need to understand that jobs that require deep analysis, critical analysis, creative solutions, and out-of-the-box thinking will not evaporate.

Credit underwriter jobs come under this category. They will survive the transformation wave, but after taking some actions. Analysts must be more efficient and productive and have the skills and knowledge to give the best solution. They can prove their worthiness by performing well in credit analyst courses and gathering certificates in risk management courses.

You can contact us through chat support or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad. We are here to help you out.

I compared top data science and machine learning courses and here’s what I think

I compared top data science and machine learning courses and here’s what I think

We’ve all seen the data science and machine learning courses advertised on the internet. But what is a data science course, exactly? And should you be doing a machine learning course instead? I compare two popular data science and machine learning courses to help you decide if one is right for you.

Data Science

It is the in-depth examination of large amounts of data in a company’s or organization’s repository. This research includes determining where the data came from, analyzing its content, and determining how this data can get used to help the company grow in the future. 

An organization’s data is always in one of two forms: structured or unstructured. When we analyze this data, we gain valuable information about business or market patterns, which gives the company a competitive advantage because they have increased their effectiveness by recognizing patterns in the data set.

Machine Learning

It is a subset of data science and is one of the most powerful tools in your arsenal. It’s also sometimes known as artificial intelligence or machine learning, but don’t let that confuse you. They all mean the same thing: techniques for making predictions about something based on past data.

If you’re new to this topic, consider what machine learning can do:

  • Predict future customer behavior by looking at past purchases (like online shopping).
  • Make recommendations based on users’ purchase history and preferences (like Netflix recommending movies based on previous viewing patterns).

What Makes These Two Techniques Different?

Data science includes machine learning. It’s more about the process of data analysis, while machine learning is more about the process of data prediction.

Data scientists rely on statistics and mathematics to analyze large amounts of information from diverse sources, but machine learning only uses math. This means that when you need to predict something based on past events or trends, you can use either technique—you just have different tools for each task!

How to choose between Data Science and Machine learning?

First, you need to know that there are two main types of data science: descriptive and predictive.

Descriptive data scientists analyze large datasets to find patterns, explain trends and predict outcomes. You can use them for marketing research, fraud detection, or predicting the weather.

Predictive models use machine learning algorithms like neural networks (a type of artificial intelligence) or decision trees (a tree-based model) that make predictions based on collected training sets—the same results over time. 

Machine learning seems more applicable in some cases (like spam filtering), while data science might have better predictive power. In any case, both of these fields are extremely exciting and evolving rapidly!

Discover Data Science and Machine Learning Career with Imarticus Learning.

Students can start their careers in data science with this certificate program in data science and machine learning. Through this curriculum, students will learn the fundamentals of data science and machine learning and the knowledge and skills required to apply these concepts in the real world.

Course Benefits For Learners:

  • This five-month program, designed by IIT faculty members, will teach students how to use Python to understand data mining and machine learning methodologies.
  • India’s top educators will deliver this data science certification course live online.
  • Students will develop a solid foundation in data science with the assistance of our online data science program.

Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

Upcoming data and analytics trends in 2022

Upcoming data and analytics trends in 2022

Data-driven decisions are the norm for today’s tech industry, and there is a subsequent demand for employees skilled in data analytics. Insights without actions are a thing of the past, which is why young professionals are turning to data analyst certification courses in addition to their education. The data and analytics trends shaping 2022 are insights and their necessary actions, real-time data analysis and decision-making, and implementation of artificial intelligence and machine learning, among others.

Why is it Important to Keep Up with Data and Analytics Trends and Learn Data Analytics?

Simply put, such courses help professionals stand out and have a better chance at job interviews. You would probably agree that choosing the right data analyst certification course is a top priority, as there can be courses that still comprise an outdated syllabus. You would want to learn data analytics from a course that encompasses the current trends in the market, and that’s how it should be.

Let’s look at some upcoming data and analytics trends to help you find the right data analyst certification course.

AI-powered Analytics

Artificial Intelligence (AI) has been gaining importance every year in each field, and data and analytics are one of the fields that stands to gain tremendously from its implication. AI has significantly improved decision-making quality by providing access to novel data sources. Amounts of data amassed by companies are forever increasing to know more about their target customers, making it challenging to analyse all of it correctly.

Machine learning (ML) and AI are powerful technologies that allow organisations to make the most out of valuable insights resulting from vast amounts of data. While these technologies are necessary now, they will soon be an essential part of organisations in the coming years. The number of organisations leveraging AI and ML will increase as they look to identify patterns, boost sales, and improve efficiency.

Data Fabric

Transforming current models into data fabric models allows businesses to store, locate, and leverage customer data more effectively. It serves as a robust framework to standardise an organisation’s data management techniques and provides consistent capabilities in multi-cloud environments. With constantly increasing data complexity, businesses will resort to this framework. This framework can reuse and combine various integration styles, technologies, and data hub skills.

The data fabric model significantly reduces maintenance, design, and deployment, lowering system complexity. Its comprehensive approach makes for more relevant customer experiences. Businesses get simplified access to more significant amounts of data, which means more informed decisions regarding customer behaviour and interactions.

Real-time Data Processing

Organisations want to act quickly, and who can blame them when a day later might be too late? Whether it pertains to developing a marketing campaign or something else, real-time data processing and decision-making are a couple of essential aspects within this list of data analytics trends. Businesses can no longer rely on or utilise data sets that are analysed monthly; this is how they stay ahead of the competition these days. There is a need for constant and continuous analysis of data sets in a bid to uncover underlying patterns or changes in customer behaviour.

Real-time data processing and decision-making require newfound adaptability and agility for teams to act on time and seize opportunities. The focus on real-time data processing, analysis, and decision-making will continue as businesses want to respond to changes as they arise to stay ahead of the competition.

Agile and Composable Data & Analytics

Composable data and analytics infrastructure have unmatched potential in terms of scale. The reasons for the popularity of composable and agile data and analytics are comprehensive resource management choices and automation options. Composable infrastructure helps speed up crucial business processes while also circumventing overprovision problems.

The data fabric model acts as the foundation required for agile and composable data and analytics. Capabilities such as driving DataOps and processing hardware are significantly improved, with reduced costs, by only providing data as needed and moving data to other storage systems.

Conclusion

Data and analytics aim to create a flexible, friendly, and smooth user experience by leveraging technologies such as AI and ML. The above data and analytics trends enable team leaders to connect their actions to the insights they receive, promote productivity and agility, encourage inter-departmental collaborations, and enhance the organisation’s analytics capabilities.

For those wondering how to become a data analyst, take a look at this data analytics certification course by Imarticus Learning. This data analytics certification course encompasses a vast syllabus with the latest data and analytics trends, specially designed to provide aspiring professionals with current industry practices and skills.

Python libraries that are hidden gems in data science

Python libraries that are hidden gems in data science

Python has exploded in the data science community in recent years. This is because it has such a robust ecosystem of libraries and tools for data scientists to use. Python has become increasingly popular as a development and research tool in the data science community. It is one of the most popular programming languages for implementing machine learning and deep learning programs.

Python’s active community and open-source packages like Pandas, Tensorflow, and Keras have led it to become The Language for Data Science. Currently, there are over 137,000 Python libraries available to programmers all over the world.

Data science is all about finding hidden patterns in data. You can use various techniques to sift through data to find relationships and meaning. Python makes data analysis easy with its various libraries that allow you to dive deep into mathematical algorithms. Many different Python libraries and tools can be used for data science.

While these libraries can help you simplify your analysis, it is challenging to learn everything about them. Most beginners miss out on Python’s lesser-known libraries, methods, and functions that can make their lives easier and their codes more efficient. However, by exploring all of Python’s features, you can set yourself apart from other programmers.

This blog is about some of these lesser-known gems in Python data science libraries that are hidden away and really should be more popular. These hidden gems include:

Mito

The Mito Python library allows us to analyze data in seconds. Mito simplifies the data framework and does not require knowledge of all Pandas’ methods and functions. It also generates code, allowing us to see which methods and functions are used.

Missingno

One can use data visualizations to manage missing values more effectively with Missingo. There are four plots in the Missingno library for visualizing data completeness: bar plots, matrix plots, heatmaps, and dendrograms. There are advantages to each method for identifying omitted data. You can locate missing values, their extent, and whether they are correlated with one another. If analyzed closely, missing values may reveal a hidden story that is often overlooked.

Bamboolib

Data analysis and visualization are the most critical but tedious processes. In Jupyter Notebook and JupyterLab, Bamboolib provides developers with a GUI for Pandas DataFrames, allowing them to integrate Python seamlessly. A hidden gem library for analyzing, imagining, and managing information, it is a brilliant and highly supportive tool. As it doesn’t require any coding knowledge, it can be used by individuals who don’t come from a programming background.

PPScore

PPScore, developed by Bamboolib developers, is a library for predicting power in datasets. PPScore is a correlation matrix. In a particular dataset, the PPScore can identify linear and non-linear relationships among columns in an asymmetric way. In PPScore, 0 represents no predictive power, and 1 illustrates perfect predictive power. This can be used instead of the correlation (matrix).

AutoViz

Data analysis and exploratory tasks can be performed using this tool. Even large datasets can be visualized with the library, which can handle even the most complicated tasks. Data visualization can be retrieved with a single code. The library automatically helps visualize JSON, CSV, and txt files.

Pillow

Pillow library extends the Python interpreter’s image processing capabilities by providing various image formats, representations, and methods for image processing. Pillow has many capabilities, such as image transformation, rotation, resizing, statistics, etc. Designed for fast access to data stored in pixels, it supports a wide range of file formats.

Dabl

In Data Analysis Baseline Library (Dabl), the boilerplate task is reduced, and the components are automated. The Scikit-Learn library inspired it. Several features of the Dabl library make it easy to analyze, process, and model data in Python. You can automate several steps of your Data Science pipeline with Dabl. In Data Science, data preprocessing, data cleaning, and feature engineering constitute 80% of the work and can be automated with Dabl.

As the data science industry grows, these libraries will give you a competitive edge. Explore these Python hidden gems and stay on the lookout for more. Data is the new oil, and models are the new refineries in the new oil age. Using data science, one can use any data to extract meaningful information. A career in data science or analytics can be a significant step forward for your career. 

You can start with the “Certificate Program in Data Science and Machine Learning,” a 5-month course. This course is designed for beginners who wish to improve their data analytics skills in Python. Learn Python online and earn a data science certification from IIT Roorkee.