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Home / Data Science & Deep Learning / Best Data Science Course Training in Bangladesh
"The Bangladesh Data Science Market will be worth 6 million dollars in 2025 and the Data Analytics Outsourcing market in Bangladesh is worth $26 Billion." - (Source). Bangladesh will undoubtedly witness around three lakh job openings in Data Science by 2021. Bangladesh is second to the United States in terms of the number of job openings in Data Science. In 2019, 97,000 positions in data science and analytics were vacant due to the lack of qualified candidates. The top sectors creating the most Data Science jobs are BFSI, Energy, Pharmaceutical, HealthCare, E-commerce, Media, and Retail. Today large companies, medium-sized companies and even startups are willing to hire data scientists in Bangladesh. The five most sought after digital skills are Big Data, Software and User Testing, Mobile Development, Cloud Computing, and Software Engineering Management.
Total Duration
4 Months
Prerequisites
360DigiTMG has introduced the most comprehensive Data Science course in Bangladesh. The various stages of the Data Science Lifecycle are explored in the trajectory of this Data Science program. This Data Science training in Bangladesh begins with an introduction to Statistics, Probability, Python, and R programming. The student will then conceptualize Data Preparation, Data Cleansing, Exploratory Data Analysis, and Data Mining (Supervised and Unsupervised). Comprehend the theory behind Feature Engineering, Feature Extraction, and Feature Selection. Participants will also learn to perform Data Mining (Supervised) with Linear Regression and Predictive Modeling with Multiple Linear Regression Techniques. Data Mining Unsupervised using Clustering, Dimension Reduction, and Association Rules are also dealt with in detail. A module is dedicated to scripting Machine Learning Algorithms and enabling Deep Learning and Neural Networks with Black Box techniques and SVM. All the stages delineated in the CRISP-DMM framework for a Data Science Project are dealt with in great depth and clarity in this course. Undoubtedly this emerges as one of the best Data Science in Bangladesh due to the live project exposure in AiSPRY. This gives a golden opportunity for students to apply the various concepts studies to a real-time situation. What is Data Science? Data science is an amalgam of methods derived from statistics, data analysis, and machine learning that are trained to extract and analyze huge volumes of structured and unstructured data. Who is a Data Scientist? A Data Scientist is a researcher who has to prepare huge volumes of big data for analysis, build complex quantitative algorithms to organize and synthesize the information, and present the findings with compelling visualizations to senior management. A Data Scientist enhances business decision making by introducing greater speed and better direction to the entire process. A Data Scientist must be a person who loves playing with numbers and figures. A strong analytical mindset coupled with strong industrial knowledge is the skill set most desired in a Data Scientist. He must possess above average communication skills and must be adept in communicating the technical concepts to non-technical people. Data Scientists need a strong foundation in Statistics, Mathematics, Linear Algebra, Computer Programming, Data Warehousing, Mining, and Modeling to build winning algorithms. Having proficiency in tools such as Python, R, R Studio, Hadoop, MapReduce, Apache Spark, Apache Pig, Java, NoSQL database, Cloud Computing, Tableau, and SAS is beneficial, but not mandatory.
In this data-driven environment certification in Data Science prepares you for the surging demand of Big Data skills and technology in all the leading industries. There is a huge career prospect available in the field of data science and this Data Science certification Programme is one of the most comprehensive Data Science courses in the industry today. This course in Bangladesh is specially designed to suit both data professionals and beginners who want to make a career in this fast-growing profession. This training will equip the students with logical and relevant programming abilities to build database models. They will be able to create simple machine learning algorithms like K-Means Clustering, Decision Trees, and Random Forest to solve problems and communicate the solutions effectively. In three months, students will also explore the key techniques such as Statistical Analysis, Regression Analysis, Data Mining, Machine Learning, Forecasting and Text Mining, and scripting algorithms for the same with Python and R programming. Understand the key concepts of Neural Networks and study Deep Learning Black Box techniques like SVM.
Block Your Time
184 hours
Live Online Sessions
150+ hours
Assignments
120 hours
Live Projects
Who Should Sign Up?
This Data Science Program follows the CRISP-ML(Q) Methodology. The premier modules are devoted to a foundational perspective of Statistics, Mathematics, Business Intelligence, and Exploratory Data Analysis. The successive modules deal with Probability Distribution, Hypothesis Testing, Data Mining Supervised, Predictive Modelling - Multiple Linear Regression, Lasso And Ridge Regression, Logistic Regression, Multinomial Regression, and Ordinal Regression. Later modules deal with Data Mining Unsupervised Learning, Recommendation Engines, Network Analytics, Machine Learning, Decision Tree and Random Forest, Text Mining, and Natural Language Processing. The final modules deal with Machine Learning - classifier techniques, Perceptron, Multilayer Perceptron, Neural Networks, Deep Learning Black-Box Techniques, SVM, Forecasting, and Time Series algorithms. This is the most enriching training program in terms of the array of topics covered.
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.
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Alumni Speak
"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.”
Venu Panjarla
AVP Technology
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There are several excellent data science training courses, and the best one for you will depend on your specific needs and goals. However, some popular options include "Data Science Bootcamps," "Machine Learning Specializations," and "Big Data Analytics Courses" offered by reputable institutions or training centers.
The prerequisites for data science courses may vary depending on the level and complexity of the course. However, a strong foundation in mathematics, statistics, and programming is typically recommended. Proficiency in languages like Python or R, and familiarity with data analysis concepts, will be advantageous.
Data science courses in Bangladesh can vary in duration. Short-term courses may last anywhere from a few days to a few weeks, while more comprehensive programs or bootcamps can span several months.
A comprehensive data science course generally covers topics like data analysis, data preprocessing, data visualization, machine learning algorithms, deep learning, big data tools (e.g., Hadoop, Spark), and practical projects to apply the learned concepts.
Yes, many institutions and platforms offer online data science training options that can be accessed from Ireland or anywhere with an internet connection. Online courses can provide flexibility and accessibility to learners.
Eligibility requirements can vary depending on the institution or platform offering the data science course. Generally, these courses are open to individuals with a background in mathematics, statistics, computer science, or related fields. Some courses may require a certain level of programming knowledge, particularly in languages like Python or R. However, there are also beginner-friendly courses designed for individuals with little to no prior experience in data science.
Prerequisites may differ based on the course level and complexity. While some introductory data science courses may have no specific prerequisites, more advanced courses might require a basic understanding of programming, statistics, and mathematics. Some institutions may offer pre-assessment tests to determine if a candidate has the necessary knowledge to benefit from their course.
Yes, many data science courses are designed to accommodate non-technical professionals who are interested in gaining data science skills. These courses often start with the fundamentals and gradually introduce technical concepts, making them accessible to learners from diverse backgrounds.
Age restrictions, if any, can vary depending on the institution or platform offering the data science course. A few publications might also have a minimum age requirement, at the same time as others may be open to freshmen of every age. It's great to check with the specific direction provider to recognize their age-related guidelines.
Yes, many data science courses in Bangladesh are open to international students. However, some institutions might have specific guidelines or visa requirements for international learners. International students are advised to review the course details and contact the course provider or institution for any specific information regarding enrollment.
The cost of data science course training in Bangladesh can vary depending on the course provider, the level of the course (beginner, intermediate, advanced), the duration, and the mode of training (online or in-person). Generally, you can expect the fees to range from [insert approximate range here].
While some online platforms offer free introductory data science courses, comprehensive and high-quality data science training usually comes with a cost. However, you may find some institutions that offer scholarships or financial aid to eligible students.
The payment options for data science course training in Bangladesh may vary depending on the course provider. Some institutions might offer the flexibility of paying in installments, while others may require full payment upfront. It's essential to inquire about payment options before enrolling.
The inclusions in the course fees depend on the training provider. Some data science courses may include study materials and access to course-related resources, while others may offer them as optional add-ons or charge an extra fee for them. Certifications might also be provided by some institutions, while others may require a separate fee for certification exams.
Each training provider may have its refund policy, including the timeframe within which you can apply for a refund and the conditions under which refunds are granted. It's essential to understand the refund policy before enrolling in the course.
The recognition of data science certifications by employers in Bangladesh can vary based on the specific certification, the reputation of the training provider, and the employer's preferences. Certifications from reputable and well-known training institutions are generally more widely recognized and valued by employers.
In most cases, yes. Reputable data science training providers in Bangladesh usually offer certifications upon successful completion of their courses. These certifications validate your participation in the training and may indicate your proficiency in the covered topics. Some providers may also offer different levels of certification based on your performance during the course.
The accreditation and certification of data science courses may vary depending on the provider. Some courses offered by universities or renowned institutions may be accredited and provide recognized certifications upon completion. On the other hand, short-term training programs offered by private institutes might not be accredited but may issue their certificates of completion.
Obtaining a data science certification can significantly enhance your job prospects both in Bangladesh and internationally. It showcases your commitment to learning and acquiring the necessary skills in the field. However, it's important to remember that certifications alone may not guarantee a job; practical experience and a strong portfolio of projects will also play a crucial role in securing data science job opportunities.
Yes, many data science courses in Bangladesh offer online certification options. Online courses provide flexibility and convenience, allowing you to learn from the comfort of your home or anywhere with an internet connection. Make sure to choose a well-established online platform or training provider with a track record of delivering high-quality content and certifications.
Many reputable data science course providers in Bangladesh offer some form of placement support to their students. Placement support may include assistance in finding job opportunities, resume review and optimization, interview preparation, and career counselling. However, the extent and quality of placement support can vary between different training institutes or platforms. It's essential to inquire about the specific placement services offered before enrolling in a course.
The effectiveness of placement support can depend on several factors, including the reputation of the training institute, the strength of their industry connections, and the success of their previous students in securing data science roles. Some institutes have dedicated career services teams that actively help students find job opportunities, while others may provide more basic assistance. It's advisable to research the institute's placement track record and speak to past students, if possible, to gauge the effectiveness of their support.
The job placements offered through a data science course can vary based on the course's curriculum, the reputation of the training institute, and the current demand for data science professionals in Bangladesh. Graduates of data science courses might find opportunities in various industries, including finance, healthcare, e-commerce, IT, telecommunications, and more. They could work for government agencies, multinational corporations, startups, or consulting firms that utilize data-driven decision-making.
While some data science training institutes may have international job placement support, the focus is usually on the local job market. If you intend to work abroad, you may need to explore additional resources and networks specific to the country or region you're interested in. Building a strong online presence, participating in global data science communities, and networking with professionals in your target country can also be helpful.
Yes, placement support is often available for both fresh graduates and experienced professionals in data science courses. For fresh graduates, the support may include entry-level job opportunities and career guidance. For experienced professionals, the placement assistance may focus on helping them transition to more advanced roles or explore new career paths in data science.
The top sectors creating the most data science jobs are BFSI, Energy, Pharmaceutical, Healthcare, E-commerce, Media, and Retail.
The average salary of a Data Scientist is $ 10.3 k per annum in Bangladesh. Professionals specializing in advanced analytics and predictive modelling can command higher salaries.
The Bangladeshn government has initiated several data science projects in the fields of Agriculture, Electricity, Water, Healthcare, Education, Road Traffic Safety and Air Pollution. The Government of Bangladesh has initiated several data science research initiatives as well.
Python and R are easy to learn and maintain and therefore, Godsend to developers in Data Science. Their extended libraries make it possible to stretch the applications of Python from Big Data Analytics to Machine Learning.
The course in Bangladesh is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of online learning. We also support e-learning as part of our curriculum.
Data Science is used for securities fraud early warning, card fraud detection systems, demand enterprise risk management, analysis of healthcare information, seismic interpretation, reservoir characterization, energy exploration, traffic control and route planning.
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
4.8
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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