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Home / Data Science & Deep Learning / Professional Data Science & AI Course with Placement Assistance
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INR 115,830 84,000
INR 115,830 79,000
"AI to contribute $16.1 trillion to the global economy by 2030. With 133 million more engaging, less repetitive jobs AI to change the workforce." - (Source). Data Science with Artificial Intelligence (AI) is a revolution in the business industry. AI is potentially being adopted in automating many jobs leading to higher productivity, less cost, and extensible solutions. It is reported by PWC in a publication that about 50% of human jobs will be taken away by the AI in the next 5 years. There is already a huge demand for AI specialists and this demand will be exponentially growing in the future. In the past few years, careers in AI have boosted concerning the demands of industries that are digitally transformed. The report of 2018 states that the requirements for AI skills have drastically doubled in the last three years, with job openings in the domain up to 119%.
This dual Professional Data Science and AI Course firmly reinforces concepts in mathematics, statistics, calculus, linear algebra, and probability. A primer on Data Mining and the use of Regression Analysis methods in Data Mining ensues. The concepts and deployment of Python programming to enable Data Mining, Machine learning are also dealt with in detail. The use of NLP libraries and OpenCV to code machine learning algorithms are detailed. The main highlight of this course is the focus on machine learning, deep learning, and neural networks. Feedforward and backward propagation in neural networks are described at length. The deployment of Activation function, Loss function, non- linear activation function is elaborated. A thorough analysis of Convolution Neural Networks (CNNs), Recurrent Neural Networks (RNNs), GANs, Reinforcement Learning, and Q learning is also facilitated in this course. This course is a comprehensive package for all IT enthusiasts who wish to design and develop AI applications in their field of study. What is Data Science? Data science is the study of data having the ideal of producing significant marketable perceptivity. It's a multidisciplinary approach to large- scale data analysis that combines generalities and styles from the fields of statistics, mathematics, artificial intelligence, and computer engineering. Thanks to this study, data scientists may now ask and get answers to queries like what happened, why it happened, what will be, as well as what differently could be done with the results. Data science is important because it combines tools, procedures, and technologies to get meaning from data. Modern businesses are drowning in it as a result of the plethora of technology which may automatically collect and store data. More data about banking, health care, electronic commerce, and other facets of mortal reality are gathered via online payment gateways & platforms. Data science has a large ecosystem including courses, degrees, and jobs today thanks to industry need. Data science is expected to increase significantly over the next few decades since it requires a multidisciplinary set of abilities & experience. What is Artificial Intelligence? Building computers and robots with the ability to think, learn, and behave in ways that would typically need human intellect or that use data of a size beyond what people can analyse is the focus of the study of artificial intelligence. Modern computer innovation is built on AI, which unlocks value for both consumers and companies. For instance, OCR, or optical character recognition, utilises AI to extract text as well as data from pictures and documents, transforming unstructured content into organised data that is suitable for commercial use and revealing insightful information. Computer science, data analytics, statistics, hardware, software engineering, languages, neurology, even psychology and philosophy are just a some of the numerous disciplines that fall under the umbrella of AI. On a practical level for commercial application, artificial intelligence (AI) is a group of technologies utilised in data analytics, predictions & forecasts, object classification, natural language processing, suggestions, intelligent data retrieval, & more. These technologies are generally based on machine learning as well as deep learning. Artificial Intelligence and Data Science Salary After enrolling for our full-time Data Science programme, which combines a comprehensive curriculum and individualised mentoring and career coaching, you could get a position in the field of data science 5 months from now. Alternatively, if you'd want something a little more adaptable, the part-time Data Science course will help you get there at a pace that works for you. A data scientist can expect to make around $116,654 a year on average. The companies ready to pay such high salaries are keen to leverage big data, which is powerful, to improve business choices. Even an entry-level salary is starting to seem desirable in this growing industry. Data scientists at the entry level salary can earn up to $93,167 yearly, while those with more experience can earn up to $142,131. Similarly, an artificial intelligence engineer makes well over $100,000 per year on average. The average yearly earnings of the US is $164,769, having a median salary over $90,000 and a high of $304,500. AI developers' pay will improve as their employment alternatives expand significantly.
The present market is all about the data. To get into this, there is a vital requirement for skilled Data Science and AI professionals. There is enormous scope for a lucrative career in this domain. By using the cutting edge and appropriate tools the freshers and professionals will be able to build algorithms and analyze huge data. By using the opportunity of individual attention given by experts at 360DigiTMG, the students will be adequately trained and will be able to understand the course very effectively. Students will be exposed to real-time projects, at the learning level only they are prepared to face the challenges that are inclined to be in industries. Data Science and AI are not confined to a specific industry, so the professionals in data science and Artificial Intelligence will have the liberty to work in the areas of their interest. The main areas where Data Science and Artificial Intelligence professionals are in demand are Medicine, Space, Robotics, Automation, Marketing, Information management, Military activities, and many more. The primary objective of Data Science and Artificial training at 360DigiTMG is to deliver skilled professionals by providing quality training, guiding them to implement and gain hands-on experience.
300 hours
Sessions
Assignments
2+2
Live Projects
Learn about insights on how data is assisting organizations to make informed data-driven decisions. Gathering the details about the problem statement would be the first step of the project. Learn the know-how of the Business understanding stage. Deep dive into the finer aspects of the management methodology to learn about objectives, constraints, success criteria, and the project charter. The essential task of understanding business Data and its characteristics is to help you plan for the upcoming stages of development. Check out the CRISP - Business Understanding here.
In this module, you will learn about dealing with the Data after the Collection. Learn to extract meaningful information about Data by performing Uni-variate analysis which is the preliminary step to churn the data. The task is also called Descriptive Analytics or also known as exploratory data analysis. In this module, you also are introduced to statistical calculations which are used to derive information along with Visualizations to show the information in graphs/plots
The raw Data collected from different sources may have different formats, values, shapes, or characteristics. Cleansing, or Data Preparation, Data Munging, Data Wrapping, etc., are the next steps in the Data handling stage. The objective of this stage is to transform the Data into an easily consumable format for the next stages of development.
Learn the preliminaries of the Mathematical / Statistical concepts which are the foundation of techniques used for churning the Data. You will revise the primary academic concepts of foundational mathematics and Linear Algebra basics. In this module, you will understand the importance of Data Optimization concepts in Machine Learning development. Check out the Mathematical Foundations here.
Data mining unsupervised techniques are used as EDA techniques to derive insights from the business data. In this first module of unsupervised learning, get introduced to clustering algorithms. Learn about different approaches for data segregation to create homogeneous groups of data. In hierarchical clustering, K means clustering is the most used clustering algorithm. Understand the different mathematical approaches to perform data segregation. Also, learn about variations in K-means clustering like K-medoids, and K-mode techniques, and learn to handle large data sets using the CLARA technique.
Dimension Reduction (PCA and SVD) / Factor Analysis Description: Learn to handle high dimensional data. The performance will be hit when the data has a high number of dimensions and machine learning techniques training becomes very complex, as part of this module you will learn to apply data reduction techniques without any variable deletion. Learn the advantages of dimensional reduction techniques. Also, learn about yet another technique called Factor Analysis.
Learn to measure the relationship between entities. Bundle offers are defined based on this measure of dependency between products. Understand the metrics Support, Confidence, and Lift used to define the rules with the help of the Apriori algorithm. Learn the pros and cons of each of the metrics used in Association rules.
The study of a network with quantifiable values is known as network analytics. The vertex and edge are the nodes and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.
Learn to analyse unstructured textual data to derive meaningful insights. Understand the language quirks to perform data cleansing, extract features using a bag of words and construct the key-value pair matrix called DTM. Learn to understand the sentiment of customers from their feedback to take appropriate actions. Advanced concepts of text mining will also be discussed which help to interpret the context of the raw text data. Topic models using LDA algorithm, emotion mining using lexicons are discussed as part of NLP module.
Revise Bayes theorem to develop a classification technique for Machine learning. In this tutorial, you will learn about joint probability and its applications. Learn how to predict whether an incoming email is spam or a ham email. Learn about Bayesian probability and its applications in solving complex business problems.
k Nearest Neighbor algorithm is a distance-based machine learning algorithm. Learn to classify the dependent variable using the appropriate k value. The KNN Classifier also known as a lazy learner is a very popular algorithm and one of the easiest for application.
In this tutorial, you will learn in detail about the continuous probability distribution. Understand the properties of a continuous random variable and its distribution under normal conditions. To identify the properties of a continuous random variable, statisticians have defined a variable as a standard, learning the properties of the standard variable and its distribution. You will learn to check if a continuous random variable is following normal distribution using a normal Q-Q plot. Learn the science behind the estimation of value for a population using sample data.
Learn to frame business statements by making assumptions. Understand how to perform testing of these assumptions to make decisions for business problems. Learn about different types of Hypothesis testing and its statistics. You will learn the different conditions of the Hypothesis table, namely Null Hypothesis, Alternative hypothesis, Type I error, and Type II error. The prerequisites for conducting a Hypothesis test, and interpretation of the results will be discussed in this module.
Data Mining supervised learning is all about making predictions for an unknown dependent variable using mathematical equations explaining the relationship with independent variables. Revisit the school math with the equation of a straight line. Learn about the components of Linear Regression with the equation of the regression line. Get introduced to Linear Regression analysis with a use case for the prediction of a continuous dependent variable. Understand about ordinary least squares technique.
In the continuation of the Regression analysis study, you will learn how to deal with multiple independent variables affecting the dependent variable. Learn about the conditions and assumptions to perform linear regression analysis and the workarounds used to follow the conditions. Understand the steps required to perform the evaluation of the model and to improvise the prediction accuracies. You will be introduced to concepts of variance and bias.
You have learned about predicting a continuous dependent variable. As part of this module, you will continue to learn Regression techniques applied to predict attribute Data. Learn about the principles of the logistic regression model, understand the sigmoid curve, and the usage of cut-off value to interpret the probable outcome of the logistic regression model. Learn about the confusion matrix and its parameters to evaluate the outcome of the prediction model. Also, learn about maximum likelihood estimation.
Learn about overfitting and underfitting conditions for prediction models developed. We need to strike the right balance between overfitting and underfitting, learn about regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions. The regression techniques of Lasso and Ridge techniques are discussed in this module.
Extension to logistic regression We have multinomial and Ordinal Logistic regression techniques used to predict multiple categorical outcomes. Understand the concept of multi-logit equations, baseline, and making classifications using probability outcomes. Learn about handling multiple categories in output variables including nominal as well as ordinal data.
As part of this module, you learn further different regression techniques used for predicting discrete data. These regression techniques are used to analyze the numeric data known as count data. Based on the discrete probability distributions namely Poisson, negative binomial distribution the regression models try to fit the data to these distributions. Alternatively, when excessive zeros exist in the dependent variable, zero-inflated models are preferred, you will learn the types of zero-inflated models used to fit excessive zeros data.
Support Vector Machines / Large-Margin / Max-Margin Classifier
Kaplan Meier method and life tables are used to estimate the time before the event occurs. Survival analysis is about analyzing the duration of time before the event. Real-time applications of survival analysis in customer churn, medical sciences, and other sectors are discussed as part of this module. Learn how survival analysis techniques can be used to understand the effect of the features on the event using the Kaplan-Meier survival plot.
Decision Tree models are some of the most powerful classifier algorithms based on classification rules. In this tutorial, you will learn about deriving the rules for classifying the dependent variable by constructing the best tree using statistical measures to capture the information from each of the attributes.
Learn about improving the reliability and accuracy of decision tree models using ensemble techniques. Bagging and Boosting are the go-to techniques in ensemble techniques. The parallel and sequential approaches taken in Bagging and Boosting methods are discussed in this module. Random forest is yet another ensemble technique constructed using multiple Decision trees and the outcome is drawn from the aggregating the results obtained from these combinations of trees. The Boosting algorithms AdaBoost and Extreme Gradient Boosting are discussed as part of this continuation module. You will also learn about stacking methods. Learn about these algorithms which are providing unprecedented accuracy and helping many aspiring data scientists win first place in various competitions such as Kaggle, CrowdAnalytix, etc.
Time series analysis is performed on the data which is collected with respect to time. The response variable is affected by time. Understand the time series components, Level, Trend, Seasonality, Noise, and methods to identify them in a time series data. The different forecasting methods available to handle the estimation of the response variable based on the condition of whether the past is equal to the future or not will be introduced in this module. In this first module of forecasting, you will learn the application of Model-based forecasting techniques.
In this continuation module of forecasting learn about data-driven forecasting techniques. Learn about ARMA and ARIMA models which combine model-based and data-driven techniques. Understand the smoothing techniques and variations of these techniques. Get introduced to the concept of de-trending and de-seasonalize the data to make it stationary. You will learn about seasonal index calculations which are used to re-seasonalize the result obtained by smoothing models.
The Perceptron Algorithm is defined based on a biological brain model. You will talk about the parameters used in the perceptron algorithm which is the foundation of developing much complex neural network models for AI applications. Understand the application of perceptron algorithms to classify binary data in a linearly separable scenario.
Neural Network is a black box technique used for deep learning models. Learn the logic of training and weights calculations using various parameters and their tuning. Understand the activation function and integration functions used in developing a Artificial Neural Network.
Learn about single-layered Perceptrons, Rosenblatt’s perceptron for weights and bias updation. You will understand the importance of learning rate and error. Walk through a toy example to understand the perceptron algorithm. Learn about the quadratic and spherical summation functions. Weights updating methods - Windrow-Hoff Learning Rule & Rosenblatt’s Perceptron.
Understand the difference between perception and MLP or ANN. Learn about error surface, challenges related to gradient descent and the practical issues related to deep learning. You will learn the implementation of MLP on MNIST dataset - multi class problem, IMDB dataset - binary classification problem, Reuters dataset - single labelled multi class classification problem and Boston Housing dataset - Regression Problem using Python and Keras.
Convolution Neural Network are the class of Deep Learning networks which are mostly applied on images. You will learn about ImageNet challenge, overview on ImageNet winning architectures, applications of CNN, problems of MLP with huge dataset.
You will understand convolution of filter on images, basic structure on convent, details about Convolution layer, Pooling layer, Fully Connected layer, Case study of AlexNet and few of the practical issues of CNN.
You will learn image processing techniques, noise reduction using moving average methods, different types of filters - smoothing the image by averaging, Gaussian filter and the disadvantages of correlation filters. You will learn about different types of filters, boundary effects, template matching, rate of change in the intensity detection, different types of noise, image sampling and interpolation techniques.
You will also learn about colors and intensity, affine transformation, projective transformation, embossing, erosion & dilation, vignette, histogram equalization, HAAR cascade for object detection, SIFT, SURF, FAST, BRIEF and seam carving.
Understand the language models for next word prediction, spell check, mobile auto-correct, speech recognition, and machine translation. You will learn the disadvantages of traditional models and MLP. Deep understanding of the architecture of RNN, RNN language model, backpropagation through time, types of RNN - one to one, one to many, many to one and many to many along with different examples for each type.
Faster object detection using YOLO models will be learnt along with setting up the environment. Learn pretrained models as well as building models from scratch.
Understand and implement Long Short-Term Memory, which is used to keep the information intact, unless the input makes them forget. You will also learn the components of LSTM - cell state, forget gate, input gate and the output gate along with the steps to process the information. Learn the difference between RNN and LSTM, Deep RNN and Deep LSTM and different terminologies. You will apply LSTM to build models for prediction.
Gated Recurrent Unit, a variant of LSTM solves this problem in RNN. You will learn the components of GRU and the steps to process the information.
You will learn about the components of Autoencoders, steps used to train the autoencoders to generate spatial vectors, types of autoencoders and generation of data using variational autoencoders. Understanding the architecture of RBM and the process involved in it.
You will learn the difference between CNN and DBN, architecture of deep belief networks, how greedy learning algorithms are used for training them and applications of DBN.
Understanding the generation of data using GAN, the architecture of the GAN - encoder and decoder, loss calculation and backpropagation, advantages and disadvantages of GAN.
You will learn to use SRGAN which uses the GAN to produce the high-resolution images from the low-resolution images. Understand about generators and discriminators.
You will learn Q-learning which is a type of reinforcement learning, exploiting using the creation of a Q table, randomly selecting an action using exploring and steps involved in learning a task by itself.
Learn to Build a speech to text and text to speech models. You will understand the steps to extract the structured speech data from a speech, convert that into text. Later use the unstructured text data to convert into speech.
Learn to Build a chatbot using generative models and retrieval models. We will understand RASA open-source and LSTM to build chatbots.
Learn the tools which automatically analyzes your data and generates candidate model pipelines customized for your predictive modeling problem.
Learn the methods and techniques which can explain the results and the solutions obtained by using deep learning algorithms.
A Large Language Model (LLM) in the context of data science typically refers to advanced natural language processing (NLP) models, which I am based on. These LLMs are designed to understand and generate human-like text, making them useful for a variety of data science tasks.
Generative AI, Diffusion Models, and Prompt Engineering are all related concepts in the field of artificial intelligence and natural language processing. Let me briefly explain each of them:
Playgrounds provide a sandbox-like setting where users can test different algorithms, models, and methodologies to gain insights and improve their skills.
DALL-E is a groundbreaking generative model in the field of data science and artificial intelligence, developed by OpenAI. The name "DALL-E" is a combination of the famous artist Salvador Dalí and the robot character WALL-E from the Pixar film.
In the middle of the ongoing debate about AI draining off jobs, there is increased attention on developing human-level AI. Pioneers in technology such as Microsoft and Google are involving ethical committees to oversee how technology impacts human lives and eliminate bias in data. Data management platforms that help organizations to get information by breaking the data silos. AI is helping organizations to produce customized services, especially in the field of the financial stream where there is shifting in the adoption of analytics for customer engagement.
In the coming year, we will find that AI platforms will be dominating the public cloud market and cloud providers, especially Google, AWS, and Microsoft will further expand their AI cloud portfolio. We will observe there would be a great shift towards real-time analytics, which would help find hidden patterns and helps companies to be more productive by making data-driven decisions. We can observe similar rapid development in the areas of IoT applications. The other trends will be observed in Patent Analytics, market sizing tools, and in Earning Transcripts.
Next Batch: 21st December 2024
3765 Learners
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Get recognised for your advanced data skills with the Professional Certification in Data Science and AI. Make your mark in the highly competitive AI talent market.
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"The training was organised properly, and our instructor was extremely conceptually sound. I enjoyed the interview preparation, and 360DigiTMG is to credit for my successful placement.”
Pavan Satya
Senior Software Engineer
"Although data sciences is a complex field, the course made it seem quite straightforward to me. This course's readings and tests were fantastic. This teacher was really beneficial. This university offers a wealth of information."
Chetan Reddy
Data Scientist
"The course's material and infrastructure are reliable. The majority of the time, they keep an eye on us. They actually assisted me in getting a job. I appreciated their help with placement. Excellent institution.”
Santosh Kumar
Business Intelligence Analyst
"Numerous advantages of the course. Thank you especially to my mentors. It feels wonderful to finally get to work.”
Kadar Nagole
"Excellent team and a good atmosphere. They truly did lead the way for me right away. My mentors are wonderful. The training materials are top-notch.”
Gowtham R
Data Engineer
"The instructors improved the sessions' interactivity and communicated well. The course has been fantastic.”
Wan Muhamad Taufik
Associate Data Scientist
"The instructors went above and beyond to allay our fears. They assigned us an enormous amount of work, including one very difficult live project. great location for studying.”
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AVP Technology
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The courses in AI and data science are also in high demand for those pursuing cutting-edge technologies. They possess the ability to use machine learning, data analysis, and predictive models, which are highly marketable across industry sectors. Despite that, success is reliant on commitment, persistent learning outside the curriculum, and taking every opportunity beyond the class to gain experience in these areas.
An artificial intelligence and data science are available to the range of people who are ready to develop their knowledge in this field. Students, professionals, researchers, and even data science enthusiasts would be able to engage in studies AI and data science majoring in fields including computer science, mathematics, engineering, statistics and social sciences. Being inquisitive, an analytical thinker, and having a desire to solve problems are determining qualities you'll need for their success.
Data science refers to the processes that use statistics and machine learning in order to draw insights from data. It includes data gathering, cleaning, analysis, and interpretation. Artificial intelligence (AI) presents AI systems which are able to do tasks that require human intelligence, including machine learning and robotics. AI is designed to create systems that can learn, adapt and act freely beyond the scope of data science that emphasizes on analyzing.
Yes, data science is in high demand across various industries. Organizations are increasingly relying on data-driven decision-making, leading to a surge in demand for professionals with expertise in data analysis, machine learning, and statistical modeling. The demand for data scientists is expected to continue growing as businesses strive to leverage data for competitive advantage and innovation.
In fact, data science is an occupation that is highly needed in many different industries. Organizations are resorting to data-driven decision making resulting in a wave of demand for professionals who are conversant with data analysis, machine learning and statistical modeling. Data scientists will remain in high demand as companies get obsessed with data for competitive advantage and innovation.
Deciding on a specific "best" institute for AI requires taking all the aforementioned considerations into account such as your location, school preferences, career plans, and personal interests. Nonetheless, there are some famous institutions across the globe that are recognized for their AI research and education. Some of these events such as 360DigiTMG and coursers were conducted solely online, alongside others.
The best stream for AI typically includes disciplines such as computer science, mathematics, statistics, engineering, and related fields. These streams provide foundational knowledge in algorithms, programming, and problem-solving, which are essential for AI development.
The qualifications for data science and AI courses vary depending on the institution and program. Generally, a bachelor's degree in a relevant field such as computer science, mathematics, statistics, or engineering is required for entry into these courses. Some advanced programs may require relevant work experience or a master's degree.
The duration to learn data science and AI varies based on prior knowledge, learning pace, and the depth of the curriculum. Short courses or bootcamps may last a few months, while comprehensive degree programs can take one to three years to complete. Online courses may offer more flexibility in terms of pace and duration.
Artificial Intelligence offers promising career prospects for the future. With advancements in technology, AI is increasingly being integrated into various sectors, including healthcare, finance, retail, and manufacturing. Professionals with expertise in AI can expect a wide range of job opportunities and competitive salaries in the evolving job market. However, staying updated with the latest advancements and continuously improving skills will be crucial for long-term success in this field.
This course on data science with AI will help you in grabbing opportunities that are in great demand. You would be suitable in the positions for Data Scientist, Business Intelligence Developer, AI researcher, Algorithm engineer, Data mining Analyst, Business Analyst.
The average salary for a Data Scientist with Artificial Intelligence (AI) skills in India is Rs.1,455,232, while at the entry-level in India it is around Rs.6,20,000, mid-level and senior-level salary could earn more than Rs.55,00,000 in India. It increases with relevant experience.
Data Science with Artificial Intelligence is the perfect solution for complex issues. This technology is used in varied fields like Banking, Fake news detection, Health care sector, Speech emotion Recognition project, and in investigating crimes.
There are many popular tools in Data Science which are used extensively like Python, R, R studio, Tableau, Tensor flow, Keras, Terax. This helps in solving Data Science algorithms.
This course is specifically designed as per the requirements of professionals and freshers. 360DigiTMG delivers classroom sessions as well as online sessions with a dedicated team of trainers and mentors.
Many industries are imbibing this trending technology in their business to boost their production. Chatbots, IoT applications, the Transportation sector, Health care, Education sector, Investigation departments, Banking are among those.
360DigiTMG offers customised corporate training programmes that suit the industry-specific needs of each company. Engage with us to design continuous learning programmes and skill development roadmaps for your employees. Together, let’s create a future-ready workforce that will enhance the competitiveness of your business.
Student Voices
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I've reached a major milestone in my Data Analytics internship with 360DigiTMG. With guidance from experienced mentors, they’ve really helped me get closer to reaching my goals. Embrace the valuable knowledge and skills gained and continue leveraging this opportunity to excel in the dynamic field of data analytics.
I'm Sai Manikanta, delighted to share my internship journey at 360DigiTMG. This internship has been a great opportunity for me to expand my limits and gain new skills. Diverse activities provided profound insights, shaping a promising future. Grateful for this opportunity, I eagerly anticipate forthcoming outcomes.
The data analytics program was truly outstanding! The meticulously structured classes and enthusiastic instructors made learning both enjoyable and engaging. With this extensive knowledge at my disposal, I am not only confident but also eager to make significant strides in the field of data analytics.
One of the best institutes for training in Hyderabad. I am done with the Data science and Machine Learning course here. Trainers are highly educational and instructive. Invaluable experience gained through live projects, enhancing technical familiarity. Additional value provided through helpful working sessions further enriches the learning journey.
The teacher and staff are highly skilled at their jobs. They teach in a way that's easy to understand and interesting. They know a lot about the subject, so learning from them is great. The teacher plans everything well and explains hard stuff with lots of examples using Excel.
It was a wonderful experience for me as an intern to work in 360digitmg. This internship had made me become an expert in the field of data analytics which had greatly motivated me and Working with real-time datasets provided invaluable experience, enhancing my skills significantly.
It was an awesome experience at 360Digitmg, offering the best resources and fostering excellent interaction. Working on real-life projects under expert supervision provided invaluable learning opportunities. Overall, it was a highly rewarding learning experience that contributed significantly to my growth and career advancement.
I found a great coaching institute in Chennai for data-related courses. I completed a successful data analytics program there. The trainers were skilled and supportive, especially Vijay, who made learning Python easy. Thanks to him and 360DigiTMG. I also learned Data Analytics with SQL, Tableau, and Excel.
360DigiTMG institute offers an exceptional learning experience, excelling in data science and machine learning. Despite lacking coding background, tutors ensured effective learning, making concepts easily understandable. Tutorial sessions covered job interview prep and case studies, with Mind maps boosting confidence. Highly recommend this Bangalore institute for data-related courses.
Excited for upcoming internships, confident in my improved skills from the program. Explored new territories and gained invaluable experience. Ready to apply newfound knowledge and continue growing in future opportunities. Grateful for the journey so far, eager for what's ahead.
360DigiTMG institute offers one place where the course curriculum is so good and teacher training, equipping students with skills for their dream job. Grateful for the internship experience, including live projects, resume building, presentation practice, and interview preparation sessions. Enhanced confidence for future interviews. Thank you, 360DigiTMG, for the invaluable learning journey.
The data analytics with python course in the best coaching centre in Chennai. Finished the course well and worked on practical tasks. This helped me build my professional experience. By participating in interview preparation and project presentation sessions, I realized that I could present myself confidently to an interview.
During my internship at 360DigiTMG, I gained invaluable experience, expanding my knowledge significantly. The opportunity provided a rich learning environment, fostering personal and professional growth. Grateful for the wonderful experience and the skills acquired, which will undoubtedly shape my future endeavours.
Great institute! Exceptional learning experience, especially in data science and machine learning. Tutors adeptly simplified complex concepts despite my coding limitations. Varied tutorial sessions prepared us for job interviews with insightful case studies. Mind maps boosted confidence. Highly recommend this Bangalore-based institute for data-related courses.
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