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- Develop Practical Knowledge skills in Data Science with Python, R Programming, Statistics, Machine Learning, Artificial Intelligence, Tableau, Deep Learning,Unix, Git, SQL.
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- Concepts: Data Science, significance of Data Science in today’s digitally-driven world, components of the Data Science lifecycle, big data and Hadoop, Machine Learning and Deep Learning, R programming and R Studio, Data Exploration, Data Manipulation, Data Visualization, Logistic Regression, Decision Trees & Random Forest, Unsupervised learning, Association Rule Mining & Recommendation Engine, Time Series Analysis, Support Vector Machine - (SVM), Naïve Bayes, Text Mining, Case Study.
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About Data Science Training Course in Chennai
Most Job Oriented DataScience Modules Covered
- R Programmming Python, SAS Artifical Intelligence
- Deep Learning Machine Learning Statistics, Naive Bayes
- Linear Algebra, CART Programming, Neural Networks Data Mining, Visualization
Is Data science be an good career option?
Is it worth being a data scientist?
Is Data Science in demand?
Will ACTE Help Me With Placements After My Data science Course Completion?
Is Data Science a good field?
What is the difference between data science and big data?
Does Data Science require coding?
Will I Be Given Sufficient Practical Training In Data science?
Is it worth learning data science in 2020?
Does data science have a future?
Top reasons to consider a career in Data science?
IntroA mixture of many fields of science that deals in formulas, patterns, statistics, math and business give birth to one of the most demanding subjects known as data science. Data science draws inspiration and its basis mainly from the fields of statistics and business intelligence and combines computer science and other modern technologies like artificial intelligence and machine learning to make smarter decisions. The data is analysed and the results of the analysis are used to draw conclusions and make decisions based on the supporting data. As such,a data scientist has a lot to offer the world of industries. In this blog, we are going to see what the data scientists do and what is the scope of data science in India and the world.
Responsibly of the data scientist.
- Collect the huge amount of data available on the internet through the company site as well as third-party sources like surveys and social media.
- Clean up the irrelevant data and store the data in a database.
- Research the data and frame questions which need to be answered.
- Use modelling, statistics and analytics programs to organize the data into a predictive model.
- Analyze the data and come up with trends and opportunities and answers for issues or problems when required.
- Write up new algorithms to solve issues if nothing existing works. This also means that data scientists have to build data science toolsif need be.
- Recommending changes to existing system and strategies.
- Presenting the analyzed results, trends, opportunities and even weaknesses in an understandable manner across various teams with the help of visualization techniques.
What you should do to be a data scientist?There are multiple paths one can take to be a data scientist. The longest route is to start with a different role, a data analyst and then work up from there based on your experience. The faster and easier way is to become a data analyst and then upskill with whatever is necessary to become a data scientist. Say, for example, that you need to work with a set of tools that are written in python. Then the first step is to take an online course on Python and gain working knowledge of it. Similar study any other programming language language that has a strong presence and use for data science. Another route, the most expensive one, is to study Masters degree in data science at a reputed institute. This is not a foolproof method and you have shell out huge amounts of money for the degree. The more affordable route and the best is to choose a certification course in data science. ACTE offers an online course on data sciencewherein you get a lot of practice time and get to do hands on projects. We also offer classes offline -data science course at our centre for learning.
General benefits of Data science
Smart decisionsIn this day and age, data-driven decisions are considered as smart decisions. And data science plays an important role in not just helping make decisions that are smarter, but also helps them do it faster. Every decision is reviewed again after some time and the results are evaluated. This way, data scientists give an approach to evaluate and improve the company’s overall performance.
Target identificationTarget identification has never been easier. Every product and service needs to target the right demographics to make the maximum benefit or sales of their product or services. As such, using analytics and available data sourced from a variety of channels, it becomes easy to identify and further refine the set of the target audience for the service or product. With this, companies can tailor their services for demographics and can increase their profitability.
Transforming risk analysisData available is humongous and analyzing this data along with predictive analysis allows us to be forewarned of certain risk scenario. We can then propose a plan of action to mitigate the risk and suggest alternative workings to achieve our goals. All this is possible only with data science tools which help us analyze huge amounts of data faster and gain actionable intelligence from it.
Average Salary earned by a data scientist in India.The salary of a data scientist averages at Rs.7,00,000/- per annum in India. Of course, you can get a better salary by improving your skills, and resume. The salary is based on many variable factors like the industry of employment, the job profile, the education, the location of the job and experience. If you have pluses on these boxes, you can get a better salary by negotiating for your worth with the recruiter.
Scope of Data Science in IndiaIndia is no stranger to progress in science and technology. We are trending in IT and healthcare and have a strong presence across a multitude of industries. These industries have a dependence on data science to make smarter decisions that are based on the data that indicate consumer preferences and help market it to the right group of people. This means that there is no limit to the scope of data science in India. The only restriction is the extent of this dependency on data science across various industries. Every industry has something to offer to the customer and data scientists find a way to help them do this efficiently and at a higher profit(That at least is the hope). Let’s see some of the industries and what data science does for them.
Scope of Data science across industries.The influence of data science is enormous in multiple sectors. Every below-mentioned industry has a high dependence on data science and offers great opportunities for a data scientist.
RecruitingA recruiter has to go through any large number of resumes at any given time, especially more so in the recruiting season. One of the biggest challenges for the recruiter is to find the right recruit based on the resumes and that is like finding the right needle in the needle stack. But this difficult job of epic proportions is made very easy with the advent of data science. Data scientists use the data from every avenue open to them, including social media and recruiting websites to find the right candidate for the position. By data mining and processing of resumes, any company can help their recruiter to find candidates faster as well as accurate. Know how to write a great resume so that your resume will be selected even stringent filters are applied with data science. This will help you get recruited as well do the recruiting.
HealthcareThis is an umbrella term for anything related to medicine and patients and diseases. Starting from more efficient diagnosis to medical research, data science has started playing a pivotal role in this sector. It provides assistance with Image analysis and research for drugs. It is also found effective to use data science to enhance customer support and assistance.
Banking and Insurance- Fraud detection and risk assessmentCustomer profiles, past applications and expenditures and many facets of personal information are collected by banks, esp for loans and insurance companies. This information if properly utilized can reduce fraud and can be used for risk assessment for loans and many other purposes. This is where data science plays an important role and makes this process easier and identifies good risk and high-risk parties. Based on these predictions, bankers can easily select candidates to issue the loans. And, in a similar fashion, insurance companies can prevent being defrauded of money.
Marketing and advertising.You can with all the data at your fingertips analyze and find out what your target audience should be to efficiently sell your service or your product. The data is used to gives demographics and interest based on which targeted advertising campaigns can be launched to convert potential leads into customers.
AirlinesThe airline industry uses data science for flight path and route analysis. With a view to reducing operating costs and improving profitability and occupancy rates, airliners took to data science to predict and assess any expected delays to the flight timings, and drive customer loyalty programs. Even the halts in between destinations and what planes to buy for higher ROI is something that is decided using the results of the data science algorithms.
Automobile industryThe Auto industry is now making strides in making driverless cars a commercial reality. A few companies have tested the technology and it is still being refined and assessed. This is a futuristic endeavour where data science along with ML and AI play a very important role. Only with all the data of where what is and how it is to kind of data is made accessible to the technology can this type of experiment be a success. As is stands, there is a scope in the auto sector where data is used for driving loyalty programs, servicing of vehicles and customer experience with tech support and many more.
Virtual Reality and Augmented RealityThis is one of the more exciting premises in the future of data science. As such VR has a close relationship with data science esp in the gaming sector. The VR technology requires data and analysis to create a setup which reflects real life and yet you are able to function, up to a level, inside the virtual world. This tech will require dependency of AI and ML. Once this tech becomes more commercially viable than it is today, data science has potentially an unlimited horizon to expand in this nascent field. The fields of artificial intelligence and data scienceare related yet worlds apart and similarly there are differences between machine learning and data science. Every field is interlinked and is used by the practitioners of the other to varying degrees and all this together makes this a very exciting enterprise to look forward to.
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Syllabus of Data Science Course in ChennaiModule 1: Introduction to Data Science with R
- What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.
- Hands-on Exercise - Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.
- Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.
- Hands-on Exercise -Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.
- Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.
- Hands-on Exercise -Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.
- Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.
- Hands-on Exercise -Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.
- Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.
- Hands-on Exercise -– Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.
- Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.
- Hands-on Exercise -Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.
- Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression
- Hands-on Exercise -Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.
- What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.
- Hands-on Exercise -Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.
- What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.
- Hands-on Exercise -Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data.
- Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation Use-cases.
- Hands-on Exercise -Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.
- introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.
- What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.
- Hands-on Exercise -Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.
- Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.
- what is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.
- Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.
- This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
Hands-on Real Time Data Science Projects
Wallmart Sales Data Set
Retail is another industry that extensively uses analytics to optimize business processes.
Flipkart Classification Dataset
This project is to forecast sales for each department and increasing labelled dataset using semi-supervised classification.
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- We have separate student’s portals for placement, here you will get all the interview schedules and we notify you through Emails.
- After completion of 70% Data Science training course content, we will arrange the interview calls to students & prepare them to F2F interaction
- Data Science Trainers assist students in developing their resume matching the current industry needs
- We have a dedicated Placement support team wing that assist students in securing placement according to their requirements
- We will schedule Mock Exams and Mock Interviews to find out the GAP in Candidate Knowledge
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About Experienced Data Science Trainer
- Our Data Science Training in Chennai. Trainers are certified professionals with 7+ years of experience in their respective domain as well as they are currently working with Top MNCs.
- As all Trainers are Data Science domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
- All our Trainers are working with companies such as Cognizant, Dell, Infosys, IBM, L&T InfoTech, TCS, HCL Technologies, etc.
- Trainers are also help candidates to get placed in their respective company by Employee Referral / Internal Hiring process.
- Our trainers are industry-experts and subject specialists who have mastered on running applications providing Best Data Science training to the students.
- We have received various prestigious awards for Data Science Training in Chennai from recognized IT organizations.
Data Science Course Reviews
ACTE is the best training institute for Data science and Data Analytics in Chennai. The trainers are well experienced and the methodology of teaching is top notch. They provide practicals along with theoretical sessions for complete understanding of the concepts. They even provide placement assistance after course completion as well.
Amazing experience. I was taught by Hari sir. He holds in depth knowledge of the subject matter and covered all the important topics that are required to be a Data Scientist in just a span of 3 months. His notes are notes quite good and helpful for interviews.I am glad I took it as it gives a quick start to your career in Data Science. Thanks ACTE at Velachery
Best DATA SCIENCE training institute in Tambaram with Realtime client projects and dedicated support team. I have taken Data science training on this January and completely happy with their teachings, projects and job support after the course completion. It's a One stop destination for your data science And AI training in Chennai.
ACTE for your career switch to Data Science..They have well experienced Trainers in ACTE who can make you industry ready. Curriculum is quite unique and includes current industry needs. They give very good job assistance also.
Its a good institute for Data Science in Porur. The teaching staff is good, they give us day wise assignments which helped me to hands on algorithms of machine learning and also provide us the backup classes. The access they provide is very helpful to listen the classes repeatedly. They provide good placement assistance. Thanks social ACTE team for your support and guidance.
Data Science Course FAQs
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Does ACTE provide placement?
- ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
- We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
- More than 3500+ students placed in last year in India & Globally
- ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
- 85% percent placement record
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Work On Live Projects?
- The entire Data Science training has been built around Real Time Implementation
- You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
- GitHub repository and Showcase to Recruiters in Interviews & Get Placed
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Why Should I Learn Data Science Course At ACTE?
- Data Science Course in ACTE is designed & conducted by Data Science experts with 10+ years of experience in the Data Science domain
- Only institution in India with the right blend of theory & practical sessions
- In-depth Course coverage for 60+ Hours
- More than 50,000+ students trust ACTE
- Affordable fees keeping students and IT working professionals in mind
- Course timings designed to suit working professionals and students
- Interview tips and training
- Resume building support
- Real-time projects and case studies
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