Login
Congrats in choosing to up-skill for your bright career! Please share correct details.
Home / Blog / Data Science / Best Data Science Course : The Complete Guide for Beginners
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
Table of Content
A large number of employment vacancies exist in the burgeoning field of data science courses. The good news is that there are specialised training programmes available that make it feasible to pursue a career in data science regardless of one's educational background or level of experience. Statistics, mathematics, and computer science are all necessary for data science. Data Science demands a fundamental grasp of coding and programming. Among millennials, one of the top five job choices is data scientist. As data-driven judgements become more lucrative than feasible, their importance and demand are expected to increase in the future. Senior data scientists with 10 years of experience are paid between Rs. 20 and Rs. 30 per hour, while young data scientists with 5 years of experience are paid between Rs. 12 and Rs. 18 per hour. Freshmen with little to no experience may get a job as a data scientist for Rs 8–12 LPA. This training is in great demand across organisations and sectors, and there is a strong intake of applicants from different backgrounds who want to become data scientists. For anybody interested, studying data science offers a successful and rewarding career.
So how do we learn? That is the question that we ask or try to find answers about.
Also, check this Data Science Institute in Bangalore to start a career in Data Science.
Since 2012, professional training organizations or EdTech startups have been engaged in the delivery of Data Science programs in online and offline mode. Due to COVID 2019, primarily all programs have been delivered in online mode for the time being which is taught by universities across the globe. The majority of programs that are offered are master’s in Data Science. A few undergraduate programs are also available. However, the masters program is what undergraduates can go for in. Aspirants can choose from either of the two options to learn Data Science from. However, the university route demands higher time and financial investment. A lot of aspirants prefer to go to professional training and certification institutes, which also have credible universities and professional organizations accreditations. They are much quicker, cost-effective/cheaper, and help in a fast turnaround in an aspirant’s learning journey with visible and realizable impact. So, depending on the time and finance constraints, one can take an informed decision.
There is no shortage of training materials or curricula for data science. Education has become more democratic to the point that all information is readily and freely accessible. Aspirants might want to study the fundamentals independently. It is a good idea, but it won't last. An instructor always has a significant influence on the learning process of the aspirant. If one has to go further into the principles given, they can become a little serious and complicated. In order to avoid feeling overwhelmed by a torrent of information, teachers with real-world experience and practical training are required. Additionally, the teachers may guide the students in a time-efficient manner while they pursue information. Any competent and seasoned instructor will begin the course by outlining the CRISP-DM project management technique. You may read more about the CRISP-DM technique here (link). The methodology outlines each step that must be taken in order to complete a project. One must learn the ideas from start.
Can I have a successful career in this field? How can I fast pick up the essentials to land a job in data science? Hey, but what are the requirements for the programme? Am I even qualified to participate in this coaching? These are a few of the concerns an aspirant has when thinking about continuing. This is a really wise precaution to consider before making a choice. The majority of hopefuls choose their programmes based on criteria like the least expensive online Data Science degree or employment that are promised. Even before making that decision, one should thoroughly consider the course contents.
Anyone can enter this field, it has been heavily advertised and pushed. This is accurate, however a potential candidate should be aware of what it takes to become a data scientist. When fancy-sounding phrases like technology, algorithm, machine learning, etc. are used, candidates frequently have their own interpretation of eligibility in mind, which prevents them from conducting an objective assessment. Instead of becoming lost in language, one should go deeper and comprehend more.
Looking forward to becoming a Data Scientist? Check out the Data Science Course and get certified today.
The easiest approach to judge one's eligibility is to first make a list of one's goals. Why would someone ever consider becoming a data scientist? Once the answer to that query is known, one may search for more comprehensive topics to study the basics. The element of coverage could take into account things like time availability, the needed financial commitment, educational background, professional job experience, and course work. Let's examine each of these elements individually.
Mastering in Data Science tools and techniques will transform you into a professional Data Scientist. Let’s study the valuable insights to know what it takes to become a Successful Data Scientist.
A Data Scientist will become one of the most important cornerstones of every industry, according to Thomas Davenport, who made this remark in a 2012 Harvard Business Review. For this career path, there has been no turning back since that time. According to reports, data scientists in the USA make more than $200,000 annually with five years of job experience. Organisations all across the world are having trouble locating the proper talent. The need for Data Scientists has risen dramatically.
The market is in a frenzy as a result of the demand. Everyone wants a piece of this burgeoning industry, whether they have job experience or not. Regardless of their backgrounds, the majority of academics and professionals are upgrading their skills in order to take this course. We now think that everyone can earn a Master in Data Science thanks to the market's excitement. And it's accurate!! There is no shortage of people studying data science. This has given educational and affiliate companies a big boost.
The market has been overrun by Data Science textbooks, tools, and syllabi in recent years. Every aspect of data science tells a compelling professional narrative. However, students and aspirants must first stand back and assess their prior academic training and professional experience. There are two categories of candidates' backgrounds: technical and non-technical. This is crucial since developing one's key competencies and abilities is necessary to pursue a career as a data scientist. This course's universe is vast, intricate, and always growing.
The further one goes, the more intensive and technically advanced it becomes, eventually becoming more engineering focused. In order to obtain knowledge in the technical or non-technical stream, one can first choose and build the learning path. People with technical backgrounds, such as engineers, can grow expertise into more technology-oriented difficulties. People with backgrounds in the liberal arts or business might seek to building experience in addressing business challenges that could be quite statistically inclined.
After deciding which stream to follow, the aspirant must carefully consider how much time can be dedicated to their schooling. Programmes in data science are available from several institutions and professional organisations. These courses cover a lot of ground.
The global market for data science is slowly but surely maturing. The skill sets needed to become a Data Scientist have grown. Organisations now demand that data scientists not only be skilled at doing statistical analyses or developing machine learning models but also have knowledge of databases, cloud deployment, automation, and model scalability. Therefore, it is crucial to pick a programme that takes into account all the needs. Investigating pooled internet job marketplaces will allow one to learn more about the work position criteria. The job descriptions provide firsthand knowledge of what businesses now want of a data scientist. Organisations frequently request talents that are not necessary for the employment function in order to develop capacity for the future. An aspirant must pick a well curated programme that realistically addresses and satisfies the market need in order to live up to such expectations.
Earn yourself a promising career in data science by enrolling in the Data Science Classes in Pune offered by 360DigiTMG.
Organisations anticipate that Data Scientists will develop Artificial Intelligence by utilising their practical knowledge in fundamental statistics.
Data Science learner needs to ensure that the course covers the below aspects:
A Data Scientist will be prepared with a solid foundation if they receive training in data science that covers all of these topics. Deep understanding of the fundamentals of statistics, mathematics, and machine learning algorithms is required, as well as extensive hands-on practise through several tasks related to each topic, in order to reinforce one's learning. One becomes better the more they practise. An aspirant data scientist should make sure the data science programme they choose will let them work on actual, live projects that can help them further their career and education. To create a fantastic portfolio to display, one has to work on several projects of this nature. Please avoid the error of constructing your portfolio with tried-and-true case studies like MNIST, Titanic, or Iris. Work on live hackathons organised by websites like Kaggle to gain additional practical experience. Make sure to post your profile on GitHub and Kaggle. It is crucial to create a LinkedIn professional networking profile that highlights your experience. Prospective Data Scientist job candidates are typically contacted by recruiters via professional networks.
Although exceedingly challenging, the path to become a data scientist is also tremendously rewarding. Data scientists are highly recognised and respected in the market since they execute arduous work for organisations. The Data Scientists are given large pay checks as a sign of appreciation. In conclusion, the moment is excellent for becoming a data scientist, but one must be determined and committed to working hard.
In conclusion, it is a terrific moment to be a data scientist, but one must have the fortitude and commitment to allow enough time for oneself to understand and practise the aforementioned ideas and techniques. The only way to excel as a data scientist is to follow that path.
This course espouses the CRISP-DM Project Management Methodology. A primer on statistics, DATA VISUALIZATION, plots, and Inferential Statistics, and Probability Distribution is contained in the premier modules of the course. The subsequent modules deal with Exploratory Data Analysis, Hypothesis Testing, and Data Mining Supervised Learning-enabled with Linear Regression and OLS. The following modules focus on the various regression models. We learn to enable Predictive Modeling with Multiple Linear Regression. The merits of Lasso and Ridge Regression, Logistic Regression, Multinomial Regression, and Advanced Regression For Count Data are explored. Data Mining Unsupervised Learning is the fulcrum of the next three modules. The various approaches used to enable the same like Clustering, Dimension Reduction, and Association Rules are elaborated in-depth with appropriate algorithms. The workings of Recommendation Engines and the key concepts of Network Analytics are also detailed. This Data Science Courses in India lends focus to Machine Learning algorithms like k-NN Classifier, Decision Tree and Random Forest, Ensemble Techniques- Bagging and Boosting, AdaBoost, Extreme Gradient Boosting, and Naive Bayes algorithm. Text Mining and Natural Language Processing also feature in the course curriculum. The building blocks of Neural Networks -ANN and Deep Learning Black Box Techniques like CNN, RNN, and SVM are also described in great detail. The concluding modules include model-driven and data-driven algorithm development for forecasting and Time Series Analysis. This is the most comprehensive course from the best data science training institute in India.
Learn about insights on how data is assisting organizations to make informed data-driven decisions. Data is treated as the new oil for all the industries and sectors which keep organizations ahead in the competition. Learn the application of Big Data Analytics in real-time, you will understand the need for analytics with a use case. Also, learn about the best project management methodology for Data Mining - CRISP-DM at a high level.
Data Science project management methodology, CRISP-DM will be explained in this module in finer detail. Learn about Data Collection, Data Cleansing, Data Preparation, Data Munging, Data Wrapping, etc. Learn about the preliminary steps taken to churn the data, known as exploratory data analysis. In this module, you also are introduced to statistical calculations which are used to derive information from data. We will begin to understand how to perform a descriptive analysis.
Learn about various statistical calculations used to capture business moments for enabling decision makers to make data driven decisions. You will learn about the distribution of the data and its shape using these calculations. Understand to intercept information by representing data by visuals. Also learn about Univariate analysis, Bivariate analysis and Multivariate analysis.
Data Visualization helps understand the patterns or anomalies in the data easily and learn about various graphical representations in this module. Understand the terms univariate and bivariate and the plots used to analyze in 2D dimensions. Understand how to derive conclusions on business problems using calculations performed on sample data. You will learn the concepts to deal with the variations that arise while analyzing different samples for the same population using the central limit theorem.
Want to learn more about data science? Enroll in the Best Data Science courses in Chennai to do so.
In this tutorial you will learn in detail about 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, 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 prediction of a continuous dependent variable. Understand about ordinary least squares technique.
In the continuation to 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.
Watch Free Videos on Youtube
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 Lasso and Ridge techniques are discussed in this module .
You have learnt 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, the usage of cutoff 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.
Extension to logistic regression We have a multinomial regression technique used to predict a multiple categorical outcome. 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.
k Nearest Neighbor algorithm is distance based machine learning algorithm. Learn to classify the dependent variable using the appropriate k value. The k-NN classifier also known as lazy learner is a very popular algorithm and one of the easiest for application.
Decision Tree & Random forest 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. Random forest is an ensemble technique constructed using multiple Decision trees and the final outcome is drawn from the aggregating the results obtained from these combinations of trees.
Learn about improving 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.
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 the first place in various competitions such as Kaggle, CrowdAnalytix, etc.
Learn to analyse the 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 a spam or a ham email. Learn about Bayesian probability and the applications in solving complex business problems.
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 neural network.
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. Hierarchical clustering, K means clustering are most commonly used clustering algorithms. Understand the different mathematical approaches to perform data segregation. Also learn about variations in K-means clustering like K-medoids, K-mode techniques, learn to handle large data sets using CLARA technique.
Dimension Reduction (PCA) / 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 Apriori algorithm. Learn pros and cons of each of the metrics used in Association rules.
Personalized recommendations made in e-commerce are based on all the previous transactions made. Learn the science of making these recommendations using measuring similarity between customers. The various methods applied for collaborative filtering, their pros and cons, SVD method used for recommendations of movies by Netflix will be discussed as part of this module.
Study of a network with quantifiable values is known as network analytics. The vertex and edge are the node 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.
Kaplan Meier method and life tables are used to estimate the time before the event occurs. Survival analysis is about analyzing this duration or time before the event. Real-time applications of survival analysis in customer churn, medical sciences and other sectors is 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 Kaplan Meier survival plot.
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 deseasonalize the data to make it stationary. You will learn about seasonal index calculations which are used for reseasonalize the result obtained by smoothing models.
Data science has had a major impact on the globe. Data science has made a significant influence everywhere, including in the reduction of human trafficking, early autism diagnosis, the battle against global warming, and the development of sustainable corporate impact through wise decision-making. And these are only a few instances from different fields. The advantages are always changing. As a tool for commercial success as well as for the broader welfare of society, its importance has grown. Let's attempt to comprehend the true reason for its requirement.
The primary duty is to examine the data, regardless of the subject matter, in order to draw patterns and insights from it. The scope entails defining and comprehending the current issue, documenting the goal and constraints of the problem. Then, depending on availability, the pertinent data is gathered from secondary or primary sources. Data scientists must generally work around the untidy format of the data that has been acquired. The data will be sorted and pre-processed by the data scientist so that it is ready for analysis in a clear and structured manner. After carefully examining the data, the data scientist uses it to make predictions that are utilised by businesses to develop a strategy to increase profits, by the healthcare industry to identify and prevent health problems, by social scientists to comprehend how people behave, etc. Simply put, make wiser, more informed, more thoughtful judgements.
Since social media outlets opened up, there has been a data explosion. Data is provided in a variety of formats, including text, audio, and video. Exabytes are units of data generation where one exabyte is equal to 100,000 gigabytes. The era of big data and artificial intelligence was therefore born. And the requirement for using the data has multiplied. Whether it's a corporation, a government, an intelligence agency, a hospital, a not-for-profit organisation, etc. Numerous chances and possibilities have been created by data. It's interesting to note that previous statisticians would analyse data and aid organisations in improving. But when the cost of storing data dropped, cloud platforms allowed for more storage, and computing power rose, other information streams than just statistics were used, giving rise to the development of data science engineering methodologies.
Data Science therefore gained popularity as a stunning combination of computers and statistics. We can manually only model a small number of situations for any given issue, but this added capacity allows us to simulate scenarios with countless dimensions. The breadth is so broad that through supporting innovation, it is assisting our development and helping us create a better future. It enables us to create cutting-edge, ergonomic goods that provide long-term solutions for future generations. We do not dismiss the existence of additional information streams that support such advancements. However, whether the data is organised or unstructured, qualitative or quantitative, etc., it must always be reviewed at first.
Making computers acquire empathy will be very difficult, according to Jack Ma, the founder of Alibaba, the largest online retailer in China, who made this statement in an interview. Well, we concur! The power of data science and anthropology is being used by organisations like Quilt.AI to scale up the development of human empathy in robots. Recently, researchers studied young males under the age of 18 in the state of Rajasthan to better understand "facets of masculinity" and how these facets shape their behaviour towards women. They are starting a behaviour modification campaign to stop online eve-teasing and sexist behaviour utilising the data insights they obtained. Sophisticated methods utilising Deep Learning algorithms and computer vision have made it feasible to collect property taxes.
Data science is a very effective method for finding patterns in data and using those patterns to solve complicated issues. Businesses utilise it to improve customer experience, develop better marketing plans, boost operational efficiency, etc. Governments use them to improve governance and create long-lasting public policy. The healthcare sector also uses data to comprehend human health in order to assist individuals in better managing their health. The foundation of the world has evolved into this. And it will undoubtedly improve the lives of us and future generations.
The biggest benefactors of Data Science are businesses. Most of the groundbreaking innovations have been reported in the business domain. One of the primary reasons is that over the period the cost of storing and managing data has substantially reduced. Hence, the organizations can store the generated data and take benefit from it with the help of Data Scientist. The scope is very wide as the domain is very versatile. It can be easily integrated and implemented in any business scenario based on the availability of data. So, let us understand why Data Science is needed and how it can benefit a business.
The components include defining the business problem, collecting the relevant data, cleaning and preprocessing the data, driving insights, building predictive models, and using the predictions to design business strategy to drive maximum mileage out of it. This methodology is simple and easily implemented. However, every step of the method has its challenges and takes on those challenges head-on. Every department of the business generates a lot of data and with the help of the Data Science process, it is put to best use to positively impact the top and bottom line. We can try to understand the value derived by looking at a few business departments.
These are just a handful of uses of Data for business. If we look at the business from the perspective of supply chain wise or value chain wise, then we can additionally unearth innumerable benefits of Data Science for any business. What is Data Analytics? Is it similar or different from Data Science? These topics are discussed in detail below.
Data literacy is essential in the data-driven economy. To be competitive in the job market of the future, everyone is scrambling to sharpen their data abilities. But more often than not, people struggle with the decision of choosing between a Data Analytics or Data Science career. Let's first examine how to distinguish between the Data Analytics definition and the Data Science definition in order to resolve the conundrum. Read a piece about the future of data scientists.
They're both reticular in structure. The two, however, use completely different strategies and have very different outcomes. To address the questions we are aware of, data analytics focuses on descriptive and diagnostic features. What occurred, and why did it occur? The benefit of analytics is that by arranging, analysing, and presenting the data in the best way possible, we can provide insights that can be put into practise. It is possible to quickly enhance operations by implementing the insights obtained from data analytics approaches. Data exploration and analysis are done using statistical approaches. It just serves as a foundation for data science.
A combination of statistics and machine learning is data science. A data scientist uses a variety of techniques to analyse vast amounts of data to find solutions to issues that were previously unsolvable. Analytics that are predictive and prescriptive are two benefits of data science. Finding the correct questions to ask, rather than providing incisive answers, is the main goal of the data scientist. To improve the analysis and forecasting of possible trends, the data scientist explores diverse and fragmented data. The Data Scientist goes above and beyond by prescribing actions based on the derived projections for long-term strategic planning. This strengthens the importance of data science.
The pay demanded by data analysts and data scientists are significantly different because of the fundamental differences between data analytics and data science. The wages start at about INR 4.5 lakhs annually. Freshmen may earn between INR 4.5 lakhs and INR 12.5 lakhs annually, but experienced career changers may earn between INR 25 lakhs and INR 30 lakhs annually. Data analytics salaries for new hires vary from INR 1.7 lakhs to INR 6.5 lakhs annually, while they range from INR 8.5 lakhs to INR 20 lakhs annually for seasoned specialists. The intricacy of the work carried out by a Data Scientist vs a Data Analyst determines how the two streams are valued. All of the essential duties of a data analyst are covered by a data scientist, but they go far beyond and contribute additional value. However, no candidate should use pay as a criterion while choosing between the two knowledge streams. A rigorous assessment of one's skill set and motivation should be done before deciding whether to pursue a career as a Data Analyst or a Data Scientist.
We are here to help and provide further insight if you would want more information about this topic and how firms might further profit. To contact us, please click here.
Anyone who has minimum degree qualifications can choose a Data Science course. Prior knowledge of Maths and basics of Statistics and Computer applications is required.
A Data Scientist can earn up to ₹708,013 ( Average salary per annum). At entry-level, with experience of less than one year, a Data Scientist can earn around ₹510,000 per annum and, for 1 to 4 years experience, can earn up to ₹610,811 per annum. For experience between 5 to 9 years, a Data Scientist can expect to earn ₹1,004,072 per year. The salary increases with your experience and skills.
Python and R programming languages are essential statistical tools in Data Science. Apart from these, SQL, SAS, Hive are also important. Python is a general-purpose programming language, used to deploy Machine Learning and Data Engineering models, etc.
There is a massive demand for professional Data Scientists all over the world. Countries like the USA, Australia, Canada, Malaysia are adopting the latest technologies in their business to gain a competitive edge and be productive. So, the forecast for the demand for Data Scientists is going to stay in the coming years too.
Data Science is the emerging technology that minimizes human effort and makes things easier that includes coding, mathematics, statistics, and some of the latest techniques like Machine Learning, Artificial Intelligence, Data mining, and Visualization.
Data science is classified into two types - structured and unstructured data. Structured data contains numbers, dates. Unstructured data includes text, images, video, and mobile activity. Data Science plays a prominent role in predictive analytics and logistics.
This is the right time to learn Data Science. Utilize this lockdown period productively. Though there is a layoff in many companies across the world, Data Scientists are untouched with this covid19 crisis. Many companies are looking forward to hiring Data Scientists. You can opt for a certified course through an online mode of training, depending upon your schedule. Look for a training institute that provides quality training with real-time projects and assignments, because that helps you in the long run, and you can understand the concepts thoroughly.
You can become a successful Data Scientist if you have a strong will. The prerequisites for becoming a Data Scientist are that you should have a basic degree, knowledge of Maths, computers, and Statistics. If you have good communication skills, that would be an added advantage to excel in your career. The next step is to choose the best training institute from the plethora of institutes. Search for the training institute, which gives training as per the business requirements, allows working on real projects, provides assignments. Most importantly, training should be delivered by industry experts and guidance throughout the learning. I suggest that you should not fall for discounts or any other perks. The top training institutes are- 360DigiTMG, Coursera, Edureka, Simplilearn, etc.
To search for the best training institutes before you join is a good idea. Here are a few suggestions to know which institute is better. Check out for the reviews given for the training institute; you can attend the demo, you can ask them for the first three sessions for free, look at their curriculum, you can communicate with the previous batch students and take their opinion. Check for the institute which has accreditation with reputed universities and companies that will give weightage for your certification.
Data Science is a vast field that covers various aspects that include Maths, Statistics, Computer Science, and Information Technology. It deals in extracting, analyzing, and optimizing massive amounts of data. The rise of Data Science will create 12M job openings by 2026.
Now the business is data globally. Data Science is going to conquer the world by providing valuable insights from this data. The demand is going to sustain for a larger period. Data Science is gaining popularity day by day because of its enormous benefits. It helps brands to connect with customers in a personalized way and helps in the engagement of brands and building awareness of the brand. Data Science is not specific to a particular field. The applications of Data Science can be applied to any sector that includes Transportation, Manufacturing, Automation, Education, Entertainment, Healthcare, etc. Today, Data Scientists are working vigorously to innovate new technologies that help to improve and ease human work. The demand for Data Science is rapidly growing as numerous enterprises are adopting innovative technologies to enhance their productivity and efficiency.
Here is the list of a few top hirers of Data Science and Big Data professionals
360DigiTMG - Data Science, Data Scientist Course Training in Bangalore
No 23, 2nd Floor, 9th Main Rd, 22nd Cross Rd, 7th Sector, HSR Layout, Bengaluru, Karnataka 560102
1800-212-654-321
Didn’t receive OTP? Resend
Let's Connect! Please share your details here
