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iNeuron Data Science Course – A Comprehensive Guide

iNeuron is an online learning platform, which provides high-quality education in emerging technologies. The main focus of iNeuron data science course is to make high-quality education available to everyone at a low price without compromising the quality. All the programs offered by iNeuron are crafted by industry experts and highly experienced professionals. Their main focus is to provide theoretical knowledge as well as practical knowledge to the students, to prepare them for real-life challenges.

INEURON DATA SCIENCE COURSE

iNeuron data science course include high-quality virtual labs, internships and job portals. The innovative learning environment helps students to get an interactive learning experience.

They become a part of a network of peers and professionals with whom they can interact or collaborate on a project. iNeuron data science courses are beginner-friendly and also include advanced topics. It’s great for a beginner who wants to start their career and for professionals who want to upgrade their skill sets.

Mission- iNeuron aims to help make education and experiential skills available at affordable prices so that students belonging to every economic or educational background can access them.

Features of iNeuron-

  1. Courses are affordable so that everyone regardless of their economic and educational background can access high-quality education.
  2. iNeuron data science course curriculum involves an internship, which provides students with real-life skills by solving real-world problems. Students earn an experience letter at the end of the internship.
  3. Students get mentored and supported by successful entrepreneurs, industry experts and community contributors.
  4. iNeuron includes a job portal which helps recruiters to find employees and applicants to find jobs.
  5. This course includes virtual labs which offer real-time experience by practising anytime from anywhere.

Full Stack Data Science Masters

iNeuron data science course is designed to prepare students to extract valuable information from raw data sets by using different tools and methodologies of data science like statistical inference and machine learning.

The in-depth knowledge of different concepts and tools is provided to students. It includes Python, SQL tableau and machine learning algorithms. Students learn to apply theoretical knowledge by solving real-life problems in hands-on projects which helps them gain the essential skills to make informed decisions based on data.

ineuron data science course includes the complete stack required to work in data science. Furthermore, it includes machine learning operations, cloud infrastructure, and real-time industry projects.

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Why Choose Data Science as a Career?

Data science is one of the most rewarding career options in the contemporary environment. Data science as a career offers many perks.

  1. Highly paid job- Data science is a highly rewarding career. Companies pay a high salary to employees who are well-trained and skilled in data science. Every company relies on data science experts who can provide them with valuable information by analysing the raw data. Their skills and expertise are necessary for the growth of the company in this data-driven world. This is why they are ready to pay a good amount to hire such experts.
  2. Flexibility- Data science is a career that offers flexibility in work life. There are plenty of options to choose from different fields to work in. There are opportunities to work from anywhere you want. Be it a beach or a beautiful garden, you can work from anywhere in the world. The only requirement would be a strong internet connection and you are good to go. Individuals get the chance to work at their schedule at their own pace. They get to design their workspace according to their personal preferences.
  3. Job security- There are plenty of opportunities to work for various positions in various disciplines in data science. Job opportunities are not limited at all. Even though it’s highly unlikely if you can’t find a job in your local market, you can choose to do remote work from your home. You can work for international companies and clients which would add a lot of experience to your career. There are options for remote work and freelancing as well.
  4. Diverse career opportunities- A career in data science gives the choice to individuals to work for a wide variety of disciplines. Data science is applicable in almost every field like, education, healthcare, agriculture, entertainment, sports, and many other disciplines. It provides a lot of options for data science professionals to work for. They can decide which company to work for according to their interests and comfort.
  5. Global reach- The demand for data science is at an all-time high all over the world. It allows data science professionals to work for multinational companies and global clients and gain valuable experience. A data scientist can work for global companies by applying for remote work which allows them to work from anywhere they want. There is an opportunity for freelancing as well, which allows data science professionals to work for international clients for the short term.
  6. High Demand- The demand for data scientists has risen and continues to grow at a great pace. With the growth in the use of the internet, almost every business has started to generate a colossal amount of data through online surveys, social media platforms, and ad campaigns. They need to hire professionals with relevant expertise who can help the company by analyzing this data and providing them with useful insights so that they can make effective decisions to improve the performance of the company.
  7. Better decision making- Data science is a discipline that requires a person to be analytical and objective. By training to become a data scientist, individuals gain real-life problem-solving skills that help them in their careers. Companies value the skills of problem-solving and effective decision-making in their clients. These skills are also very useful in problem-solving in personal lives. By training to be analytical and objective, a person can analyze a real-life situation and make smart decisions.

Curriculum- 

The curriculum of ineuron data science course is carefully curated by industry experts and experienced professionals. it includes the following modules.

  1. ineuron data science course introduction- This includes the orientation of the ineuron data science course and an overview of the platforms and their features.
  2. Python basic building-
  • Python Keywords and identifiers
  • Comments, indentation, and statements
  • Variables and data types in Python
  • Standard Input and Output
  • Operators
  • Control flow: if else Elif
  • Control flow: while loop
  • Control flow: for loop
  • Control flow: break and continue
  1. Python data structures-
  • Strings
  • Lists, list comprehension
  • Tuples
  • Sets
  • Dictionary, dictionary comprehension
  1. Python functions-
  • Python built-in functions
  • Python user-defined functions
  • Python recursion function
  • Python lambda functions
  1. Python exception handling, logging and debugging
  • Custom exception handling
  • Logging with python
  • Debugging with python
  1. Python OOPS
  • Exception handling using try-catch block
  • Custom exception handling
  • Logging with python
  • Debugging with python
  1. Flask-
  • Flask fundamentals
  • Building rest APIs
  1. Python project with deployment
  • End To End Review Scraper Project
  • Building an app that displays current weather conditions for a specific location using OpenWeatherMap API
  • Image web scraper- Build an image Web Scraper to extract Google images
  1. Python for data science- Numpy
  • This includes key operations using Numpy
  1. Python for data science- Pandas
  • This module includes key operations on Data frames
  1. Python for visualization-
  • Getting started with Seaborn
  1. SQL basics to intermediate
  • Working with MySQL Using NeuroLabs
  • USE, DESCRIBE, SHOW TABLES
  • SELECT
  • INSERT
  • UPDATE & DELETE
  • CREATE TABLE
  • ALTER: ADD, MODIFY, DROP
  • DROP TABLE. TRUNCATE, DELETE
  • LIMIT, OFFSET
  • ORDER BY
  • DISTINCT
  • WHERE, Comparison operators, NULL
  • Logical Operators
  • Aggregate Functions: COUNT, MIN. MAX, AVG, SUM
  • GROUP BY
  • HAVING
  1. SQL intermediate to advance-
  • Join and Natural Join
  • Inner, Left, Right and Outer joins
  • Sub Queries/Nested Queries/Inner Queries
  • SQL Primary And Foreign Key
  • SQL Function And Stored Procedures
  • SQL Window Function
  • CTE In SQL
  • Normalization In SQL
  1. SQL interview questions

Discussing FAANG SQL Interview Questions

Discussing Top Product And Service-Based Companies

  1. Exploratory Data analysis- 1
  • Analyzing Bike Sharing Trends.
  • Analyzing Movie Reviews Sentiment.
  • Customer Segmentation And Effective Cross-Selling.
  1. Exploratory Data Analysis 2
  • Analyzing Wine Types And Quality.
  • Analyzing Music Trends And Recommendations.
  • Forecasting Stock And Commodity Prices
  1. Maths for data science (linear algebra)
  • Linear Systems and Gaussian Elimination
  • Matrix
  • expressing a system of linear equations via matrices.
  • Matrix- learning to solve a linear system of equations using matrix algebra.
  1. Maths for data science (linear algebra 2)
  • Projection, Least Square- Projections and how they work. Build on a foundation, using 1D 2D projections and explore the concept in higher dimensions over time.
  • Determinant and Eigens- learn to calculate the determinant of a matrix. Eigenvalues, Eigenvectors
  1. Maths for data science (probability)
  • It includes important concepts in probability theory including random variables and independence.
  1. Maths for data science (calculus)
  • Definition of a Derivative- What is a derivative? Compute simple derivatives from the definition.
  • Product and Chain Use the product and chain rules to calculate the derivatives of more complicated functions.
  • Using Derivatives to Graph Functions-Use where derivatives are positive and negative to help graph a function.
  • Identifying Maximums and Minimums-Using derivatives for finding the maximum and minimum values.
  1. Statistics 1
  • Mean, median, mode, variance and standard deviation and their practical applications
  1. Statistics 2
  • Introduction to probability distributions
  • pmf, pdf, cdf
  1. Statistics 3
  • Hypothesis testing
  • Feature engineering
  • Feature Selection
  • Handling missing values
  • Handling imbalanced data
  • Handling outliers
  • Encoding
  • Feature Scaling
  1. Machine learning(supervised- 1)
  • Al Vs ML Vs DL Vs DS
  • Types Of ML Technqiues
  • Supervised vs unsupervised and semi-supervised and reinforcement learning
  • Linear Regression
  • End To End Project With Deployment
  1. Machine learning(supervised- 2)
  • Logistic Regression
  • Task- End To End Project With Deployment
  • Support Vector Machines
  • Naive Bayes
  • Task- End To End Project With Deployment
  1. Machine learning(supervised- 3)
  • Logistic Regression
  • Task- End To End Project With Deployment
  • Support Vector Machines
  • Naive Bayes
  • Task- End To End Project With Deployment
  1. Machine Learning (unsupervised)
  • PCA
  • Kmeans Clustering
  • Hierarchical Clustering
  • Dbscan Clustering
  • Performance Metrics In Clustering
  1. Machine Learning (Time series)
  • Time Series Using fbprophet
  • Time Series Using AutoIt
  • Time Series Using Darts
  1. End-to-end ML projects with deployment
  • Developing a Comprehensive Image Scraper with Python
  • Machine Learning-Based Fault Prediction for Industrial Sensors End To End Project
  • Developing Advanced Review Scraper using Python and Data Visualization
  • ShipSage: Use of Machine Learning in Smart Shipment Price Prediction
  • GreenVision: Al-driven Forest Cover Type Classification System
  • Customer Categorizer: Use Machine Learning to Identify Hidden Market Segments
  • PhishFinder: Machine Learning-Based Phishing Classification and Identification With Bento ML and MLFOW
  1. Interview Preparation
  • Resume Discussion And Resume Preparation
  • Python Interview Questions Discussion
  • Stats Interview Questions Discussion
  • Machine Learning Interview Questions Discussion
  • Explaination of End to Projects To Interviewer
  1. Deep learning ANN-
  • Artificial Neural Network Working
  • Back Propagation In ANN
  • Chain Rule Of Derivatives
  • Vanishing Gradient Problem
  • Exploding Gradient Problem
  1. Deep learning fundamentals-
  • Different Activation functions
  • Different Types of Loss Functions
  • Different Types of Optimizers
  • Weight Initialization Techniques
  • Drap Out Layer
  • Batch Normalization
  1. Deep learning frameworks-
  • Working with TensorFlow Keras
  • Working with Pytorch
  1. Deep learning ( computer vision fundamentals)
  • CNN Fundamentals
  • Lenet-5 Variants With Research Paper And Practical
  • Alexnet Variants With Research Paper And Practical
  1. Deep learning (image classification and transfer learning)
  • Googlenet Variants With Research Paper And Practical
  • Vggnet Variants With Research Paper And Practical
  • Resnet Variants With Research Paper And Practical
  1. Deep learning (computer vision- object detection)

Advanced algorithms to perform object detection

  1. Deep learning (computer vision- segmentation tracking)

Advanced algorithms for Image segmentation

Advanced algorithms for object tracking

  1. Deep learning (NLP-1)

NLP with machine learning

NLP with recurrent neural network and its variants

  1. Deep learning (NLP-2)

NLP with sequence models

NLP with attention models

  1. End to end Deep learning projects with deployment-

Development of audio classification system for better speech recognition

Developing a robust helmet detection system using computer vision

Developing a High-Quality Text-to-Speech System with Advanced NLP Techniques

Al-Enabled Object Detection for Improved Industrial Safety

  1. Big data- Hadoop
  2. Big data- Spark
  3. Data analytics- Power BI
  4. Data analytics- Tableau
  5. Interview Preparation

Projects-

Developing a helmet detection system using computer vision for real-time identification, ensuring safety compliance.

Developing a high-quality text-to-speech system by using NLP techniques.

AI detection for improved industrial safety

More Information About iNeuron Data Science Course-

After subscribing to iNeuron data science course, students get two years of access to the dashboard.

During the iNeuron data science course, the projects from every module get assessed and students get personalized reviews.

Students get all the course materials including projects and videos

A community of peers and mentors helps students to learn from them by collaborating on projects.

Students get access to neurolab which provides practical learning experience through practice.

All modules include quizzes to assess the proficiency of students.

Students get guidance from expert mentors and industry experts.

Students can work in many roles like data scientists, machine learning engineers, data engineers, big data engineers etc.

The course is available online.

Some of the skills one can gain from ineuron data science course are data collection and cleaning, data exploration, analysis, statistical analysis, and feature engineering.

Ineuron data science course is beginner-friendly, so anyone can start learning without previous experience.

Tools Taught in This Course Include-

  • Python
  • Flask
  • NumPy
  • Pandas
  • MongoDB
  • Hadoop
  • Spark
  • Tableau
  • Power BI

Certification-

After completion of the assignments and quizzes, students can receive the certificate from ineuron data science institute. The minimum score required to get the certificate is 60 percent in both assignments and quizzes.

Furthermore, iNeuron data science institute has helped more than 10000 students in getting hired. The highest package reached 50 lakhs. There are more than 200 hiring companies that have partnered with iNeuron which assures employment for students after completion of this course.

Alumni of iNeuron data science institute got placed in some of the best multinational companies and institutions like Amazon, Google, Hitachi, Microsoft, PayPal, and many other well-renowned companies.

Data Science Applications in Different Fields-

  1. Marketing and advertising- Recently after the revolution in the use of the internet, the field of marketing and advertising has become all about data science tools and techniques. Various data science methodologies are used in analyzing the data of consumers which is collected from surveys and social media. By analyzing this data, the demand for various products and the pain points of customers can be acknowledged which would help in creating an effective ad campaign.
  2. Manufacturing and production- In manufacturing and production, it’s very important to optimize the overall efficiency of the operations to ensure the optimum performance of the company. Statistical analysis techniques are widely used to monitor the efficiency of operations by detecting of defects so that a prevention plan can be established. Apart from this, data science has applications in keeping track of the condition of machinery so that the date for maintenance can be determined before it breaks down completely.
  3. Healthcare- The healthcare industry requires applications of data science to save the lives of patients. The past data of the patient’s medical history which consists of diseases, genomic data, drugs, and previous treatments can help healthcare providers to create personalized treatment strategies for each patient. Statistical inference is used in the development of drugs to prevent defects. Data science methodologies are used to prevent widespread disease by analyzing the collective data of a population and providing them with guidelines and appropriate treatments.
  4. Transportation- Travel agencies use different applications of data science to analyze the costs of traveling, the date for traveling, and the best route to travel. The best possible way to travel can be identified by analyzing previous travel records and the cost of travel can be optimised. Authorities of traffic can use data science tools to analyze the hotspots of congestion so that they can divert the traffic in different directions or optimize the time for a signal change.
  5. Retail- In retail services, the data on consumer behaviour can be gathered by surveys and sales records. This can provide valuable information about the consumption patterns of different segments of customers. This helps retailers to get an idea about the demand for different products so that they can optimize inventory by ordering the exact amount of products. This way by use of data science retailers save money and make a profit.
  6. Education- There are many applications of data science in the field of education. It is used by many schools, colleges, and other institutions to improve the overall performance of students and the institution. Mainly it is used to keep records of the performance of students. Later this data which includes their test marks, attendance, and extracurricular activities, can be analyzed to help the students improve their performance and identify the students in need of special attention from faculty. This data can be used to identify the weaknesses and strengths of students which would help them improve and reach their full potential.
  7. Finance- In the world of finance, data science has many applications. Many businesses use data science techniques to predict the fluctuations in the market by analyzing the previous patterns of the behavior of the market. This way companies can prepare themselves for different situations and optimize the resources according to that. Applications of machine learning are used for trading by leveraging the previous data of the stock market to use it to generate profits.

Frequently Asked Questions-

1. Are there any prerequisites for enrolling in the iNeuron Data Science Institute?

Basic knowledge of programming and mathematics is recommended to enroll in iNeuron data science institute.

2. Does the iNeuron data science institute offer hands-on projects?

Yes, the iNeuron data science institute includes multiple hands-on projects and case studies to ensure practical learning.

3. Are there any certifications provided upon completion?

Yes, participants receive a certificate of completion from iNeuron Data Science Institute after finishing the course and meeting all requirements.

Conclusion-

iNeuron Data Science Institute is one of the best digital platforms providing courses on job-relevant skills and expertise to students of all economic and educational backgrounds. They offer courses at a very affordable price without compromising the quality of the education. They provide thorough knowledge to students without needing any prior knowledge of the subject.

iNeuron data science course is a great way to begin a career in the field of data science. Data science is considered to be a highly rewarding career option as it is in very high demand and is expected to stay that way for a very long time. By enrolling in this very affordable course, students can learn all job-relevant skills that would help them to become job-ready after completion of the course.

Hello there, I am a graduate in Dairy Science and Food Technology. I have a passion for writing informative articles on topics such as health and nutrition, and I also possess a knack for storytelling. Currently, I am working as an intern at IIM Skills and looking forward to making meaningful contributions.

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