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Data Science Training in Chandigarh

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  • By taking this course, you'll learn how to analyze data, use machine learning techniques, and understand statistics in the data science field.
  • We provide students with an overview of the roles and skills involved in data science in this section. Further, you will learn how to customize your data in a way that relates directly to the requirements of the target audience.
  • Here you will find a section dedicated to different types of data analysis. A data science project can take a number of different forms, including execution, planning, and presentation. Utilising these tools and techniques will enable you to get the most out of your data.
  • The completion of this course will allow you to gain a deeper understanding of data science. The course covers a variety of topics including data science, data engineering, and product analysis.
  • The goal of this course is to teach you how to gather and analyze data using tools like R, Python, and the command line. In addition to market analysis and A/B testing, other topics are discussed.
  • A list of well-known technology companies including Amazon, Google, Square, Microsoft, and Facebook will join other well-known companies at this year's event.
  • In addition, each question is answered, as well as information about how that question was answered. The program will also benefit you at work in addition to your exam preparation.
  • Knowing these topics beforehand will come in handy if you're considering attending an interview. There will be both theoretical sessions as well as practical ones.
  • It is important to take these courses in order to gain an understanding of the subject matter. The training programs we offer equip students with skills required for job interviews, provide them with employment opportunities, and prepare them for job interviews.
  • 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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Course Objectives

Entry-level Data Scientists salaries are as motivating because of the job itself. If you'll crack an amazing data science spot or Google data science spot, the expertise you may gather here will offer purchase to your career, so there would be no trying back. Data science may be a booming field and plenty of maybe having the thought to modify thanks to lucrative job roles. However, you wish to be able to make a case for your career transition. With these in mind, you'll become a Data Science while not expertise.

The syllabus of Data Science is composed of three main elements: Big Data, Machine Learning, and Modelling in Data Science. The main topics in the Data Science program are Statistics, Coding, Business Intelligence, Data Structures, Mathematics, Machine Learning, Algorithms, amongst others.

You do not want quite a data science certificate to get a job in data science. You should select your learning platform that supported the abilities it teaches, not the certificate problem, as a result of recruiters simply do not care a lot about any data science certification. To boot, the program as a full offers a comparatively reasonable, legitimate, esteemed, and versatile thanks to learning the essential skills and experience required to interrupt into the sphere of data science.

Big data scientists shrewdness to use their skills in a scientific discipline, statistics, programming, and different connected subjects to arrange big data sets. Then, they apply their data to uncover solutions hidden within the data required on business challenges and goals. It's in Demand. Data Science is greatly in demand:
  • The abundance of Positions.
  • An extremely Paid Career.
  • Data Science is flexible.
  • Data Science Makes information higher.
  • Data Scientists are extremely Prestigious.
  • No additional Boring Tasks.
  • Choose the proper role.
  • Take up a Course and Complete it.
  • Choose a Tool / Language and stick with it.
  • Join a contemporary.
  • Focus on sensible applications and not simply theory.
  • Follow the proper resources.
  • Work on your communication skills.
  • Intro to Data Science and its importance.
  • Data Science life cycle and data acquisition.
  • Experimentation, evaluation, and project deployment tools.
  • Multiple Machine Learning algorithms.
  • Predictive analytics and segmentation utilizing clustering.
  • Fundamentals of Big Data Hadoop.
  • Roles and responsibilities of a Data Scientist.

To become a data scientist you'll earn an academic degree in applied science, Social sciences, Physical sciences, and Statistics. The foremost common fields of study are arithmetic and Statistics (32%), followed by applied science (19%) and Engineering (16%). Data Science is that the most well-liked job in the IT sector with terribly high salaries. Several students in the least levels wish to require half in information science. You'll attend online courses from your home and become a data scientist.

Can a non-science student become a Data Scientist?

How ever, though you do not have strong secret writing data and a special degree in data science, you'll still become a data scientist. With sensible learning capability, you'll be a data scientist while not a degree in it. If you're a recent candidate you can not get the post of a data scientist. Excellent news that you simply cannot become a data scientist at once doesn't mean that you simply cannot become a data scientist ever. You'll start within the trade and work your high. That's the simplest approach if truth learns.

What is required to be a Data Analyst?

To become a data analyst, you need to initial earn an academic degree, which may be a demand for many of the entry-level data analyst positions. The relevant disciplines include Finance, Economics, arithmetic, Statistics, applied science, and data management. You'll earn an academic degree in applied science, Social sciences, Physical sciences, and Statistics. Data Science and that they also undertake online training to learn a special ability like a way to use Hadoop or large data querying.

What are the benefits of being a Data Scientist?

In the area of advanced promoting, the top positions are:
  • Excellent job of the century.
  • Freedom to figure.
  • A chance to figure with massive brands.
  • The payoff is handsome.
  • Proper training and certification course.
  • Data science jobs in demand.
  • Different roles within the trade.
  • A safe career to pursue.

What does a Data Scientist do?

Data Scientists apply machine learning to find hidden patterns in large volumes of raw data to cast light on real difficulties. This entails several steps. First, they must know a suitable problem. Next, they decide what data are needed to solve such a situation and figure out how to get the data. Once they get the data, they need to clean the data. The data may not be formatted correctly, it might have additional optional data, it might be dropping entries, or some data might be wrong. Data Scientists must, therefore, do certain the data is correct before they explain the data.

How can I be a good Data Scientist?

  • Analytical mental attitude.
  • Domain data.
  • Problem-finding Skills.
  • Statistical And Programming Skills.
  • Solving Real-World issues.
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Overview of Data science Training in Chandigarh

Data science is one of the fastest-growing fields in digital technology. Companies are preparing to make data-driven decisions as a result of a massive movement towards digital transformation. For this reason, they are continuously on the lookout for competent data scientists to join this new, demanding workplace. Master data science skills by learning the theory and putting it into practise with our Data Science worldwide certification programme. This course will help you learn data science skills and be more confident in interviews if you are searching for a career in the data science sector.


Additional Info

Career path in DataScience developer:

According to the Bureau of Labor Statistics, most laptop {and information|and knowledge|and information} analysis scientists — as well as data scientists — “need a degree in computing or connected field, [like] laptop engineering.” A master's program can take you 2 years, once earning a four-year degree. If so, you would possibly wish to contemplate a knowledge soul career path..... In according that information science was the #1 most promising career path within the U.S., with a fifty-six p.c year-over-year rate of growth. From 2016 to 2019, Glassdoor graded information soul collectively of the fifty Best Jobs in America. Data Science won't solely provide you with a good career however also will assist you in personal growth. you'll be able to have a problem-solving angle. Since several information Science roles bridge IT and Management, you'll be able to relish the simplest of each worlds.

Data science Certification Training and Exam and path:

1. Hollow EMC well-tried skilled Certification Program:- Basically, hollow EMC offers a knowledge science associate certification. That guarantees a active, professional person approach. That describes because the “industry’s most comprehensive learning and certification program.” As before long as you pass this information soul certification, you’re thought of “Proven skilled,”.

2. Certified Analytics skilled:- Generally, CAP offers a vendor-neutral information soul certification. That shows recruiters and managers that you just aren’t biased to specific software package. Also, it shows that you just have a broad vary of information in your field. It helps you to balance out your additional specific, well-honed skillets.

3. SAS Academy for information Science:- As SAS Academy for information Science includes 3 programs. One that focuses on huge information skills; Another that focuses on information analytics skills, and A third program that features each information analytics and massive information skills.

4. Microsoft Certified Solutions knowledgeable (MCSE):- As MCSE certifications cowl a large kind of IT specialties and skills. As skills square measure supported information science. Also, for its information soul certification, Microsoft offers 2 courses; one that focuses on business applications, and another that focuses on information management and analytics.

5. Cloudera Certified Associate (CCA):- Basically, this communication indicates your basic data as a developer. And additionally as Associate in Nursing administrator of Cloudera’s enterprise software package. Once you've got passed this communication. Then your earning certification is best to prove yourself. it'll show your performance to employers.

6. Cloudera Certified Professional: CCP information Engineer:- Once you earn your CCA, then you'll move onto the CCP communication. Cloudera touts collectively of the foremost “demanding performance-based certifications.” Also, its main specialize in mastering skills and testing your experience.

7. Information Science Certificate – Harvard Extension faculty:- To earn a certificate from this faculty, there's one condition to be consummated. Firstly, you've got to finish this course. After this, you've got to earn a minimum of a B grade in four certification courses at intervals 3 years. Also, you'll select 2 electives from a choose cluster.

8. Amazon AWS huge information Certification:- If you've got a minimum of two years of expertise of operating within the AWS setting, and you wish to transition into the analysis of complicated information, then Amazon’s AWS huge information Certification is right for you.

Industry Trends

1. Health Care:- Building a career within the health care sector could be a delicate matter. Thus, if you opt to enter it, make sure that you are able to work around information that would be associated with folks fighting their lives. So, operating during this field could be a humanitarian issue to try to, and it's unneeded to mention you've got to cautious whereas coming up with your information science strategy to supply the foremost helpful conclusions to information issues.

2. Telecommunications Sector:- Mind Commerce anticipates huge information and information science trade to escort the telecommunication sector to expand at a compound annual rate of growth of fifty p.c, with the annual revenue going to $5.4 Billion by the tip of 2019. Information storage prices have gone manner down and laptop process power goes skyward thanks to simply accessible analytics software package. Hence, the work of a knowledge analyst has become a touch easier.


3. Web trade:- The internet trade is gaining strength as we tend to speak as a results of the information surge caused by subtle technology, huge information in conjunction with cloud computing. there's Associate in Nursing cryptic quantity of knowledge within the hands of knowledge scientists that they're victimization to style customized recommendation, undertake sentiment analysis, video analysis, etc. Hence, the web trade, e-commerce, Associate in Nursing social networks square measure thriving to an out of the question level with billions and billions of individuals victimization the web, posting photos and videos on social media and creating Google searches each second of the day.

4. Energy Sector:- Data science and massive information square measure exhibiting their transformational powers to reshape the Energy vertical. information analysts square measure proving to be extremely helpful for locating unconventional energy sources, reducing prices and saving cash on exploration and drilling, increasing effectiveness, avoiding power outages, enhancing productivity, and so on. The information science trade is additionally curbed the probabilities of accidents by providing higher repairs.

5. Automotive trade:- Data science professionals have an enormous role to play within the automotive trade. computer science, machine learning, information science square measure the most technologies facultative this sector to combat its varied challenges and regenerate itself. operating during this sector can provide you with a good likelihood to widen the vary of your skills by making merchandise with improvement and automatic learning.

Top framework or technologies and major tool in Data Science:

1. SAS:- SAS (Statistical Analysis System) is one in all the oldest knowledge Science tools within the market. One will perform granular analysis of matter knowledge and may generate perceptive reports via SAS. Several knowledge scientists like the visually appealing reports generated by SAS. Besides knowledge analysis, SAS is additionally wont to access/retrieve knowledge from numerous sources. It's wide used for multiple knowledge Science activities like data processing, statistic analysis, political economy, business intelligence, etc. SAS is platform-independent and is additionally used for remote computing.

2. APACHE HADOOP:- It is associate ASCII text file software system-wide used for the data processing of knowledge. Any giant file is distributed/split into chunks and so handed over to varied nodes. The clusters of nodes square measure then used for data processing by Hadoop. Hadoop consists of a distributed filing system accountable for dividing the info into chunks and distributing it to varied nodes. Besides the Hadoop File Distribution System, several alternative Hadoop elements square measure want to parallelly method knowledge, like Hadoop YARN, Hadoop MapReduce, and Hadoop Common.

3. TABLEAU:- Tableau may be a knowledge image tool that assists in decision-making and knowledge analysis. {you will|you'll|you'll be able to} represent knowledge visually in less time by Tableau in order that everybody can know it. Advanced knowledge analytics issues may be solved in less time victimization Tableau. You don’t ought to worry concerning fitting the info whereas victimization Tableau and may keep centered on wealthy insights.

4. TENSORFLOW:- TensorFlow is wide used with numerous new-age technologies like knowledge Science, Machine Learning, computer science, etc. TensorFlow may be a Python library that you just will use for building and coaching knowledge Science models. You'll be able to take knowledge image to future level with the help of TensorFlow.

5. BIGML:- BigML is employed for building datasets and so sharing them simply with alternative systems. At first developed for Machine Learning (ML), Big ML is wide used for making sensible knowledge Science algorithms. You'll be able to simply classify knowledge and notice the anomalies/outliers within the knowledge set victimization Big ML.

6. KNIME:- Knime is one in all the wide used knowledge Science tools for knowledge coverage, mining, and analysis. Its ability to perform knowledge extraction and transformation makes it one in all the essential tools utilized in knowledge Science. The Knime platform is ASCII text file and liberated to use in numerous components of the globe.

7. RAPIDMINER:- RapidMiner may be a wide used knowledge Science software system tool because of its capability to produce an acceptable atmosphere for knowledge preparation. Any knowledge Science/ML model may be ready from scratch victimization RapidMiner. Knowledge scientists will track knowledge in period of time victimization RapidMiner and may perform high-end analytics.

8. EXCEL:- Part of Microsoft’s workplace tools, surpass is one in all the simplest tools for knowledge Science freshers. It additionally helps in understanding the fundamentals of knowledge Science before getting into high-end analytics. it's one in all the essential tools employed by knowledge scientists for knowledge image. Surpass represents the info during an easy method victimization rows and columns to be understood even by non-technical users.

Future in Data Science developer and trending:

The scope of knowledge Science is growing with each passing year. From 2008 to 2020, individuals across the world have stepped on the medical aid age. the large growth of knowledge provides a glimpse of the long run scope of knowledge in Science in Asian country.

Health care sector:- There is a large demand of knowledge scientists within the health care sector as a result of they produce a great deal of knowledge on a day to day. endeavor an enormous quantity of knowledge isn't doable by any unskilled candidate. Hospitals have to be compelled to keep a record of patients’ case history, bills, employees personal history, and far alternative data. knowledge scientists are becoming employed within the medical sector to reinforce the standard and safety of the info.

Transport Sector:- The transport sector needs an individual to research the info collected through traveler tally systems, quality management, location system, fare collection, and ticketing.

E-commerce:- The e-commerce business is booming simply because of knowledge scientists United Nations agency analyze the info and build bespoken recommendation lists for providing nice results to end-users.

Data Science Training Key Features

1. information Exploration:- It is the foremost vital step, as this step consumes the foremost quantity of your time. Around seventy per cent of the time is spent on information exploration. The most ingredient for information science is information, thus after we get information, it's rarely that information is during a correct structured type. There's a great deal of noise gift within the information. The noise here suggests that a great deal of unwanted information that's not needed. thus, what we tend to|can we|will we} knock off this step? This step involves sampling and transformation of knowledge within which we check the observations (rows) and options (columns) and take away the noise by exploitation applied math ways. This step is additionally accustomed check the connection among varied features(columns) within the information set; by the connection, we have a tendency to mean whether the features(columns) square measure enthusiastic about different|one another} or freelance of every other, whether there square measure missing values information or not. thus, primarily, the info is remodeled and readied for additional use. thus, this is often one in every of the foremost long steps.

2. Modeling:- So, by now, our information is ready and prepared to travel. This is often the second step, wherever we have a tendency to truly use Machine Learning algorithms. Here we have a tendency to truly work the info into the model. The choice of a model depends on the sort of knowledge we've got and also the business demand. As an example, the model choice for recommending a piece to a client are going to be totally different from the model needed for predicting the amount of articles that may be sold-out on a specific day. Once the model is determined, we have a tendency to work the info into the model.

3. Testing the Model:- It is succeeding step and extremely vital regarding the performance of the model. The model is checked with test information to ascertain the model’s accuracy and alternative characteristics and build the specified changes within the model to induce the required result. Just in case we have a tendency to don't get the required accuracy, we are able to once more head to step 2(modelling), choose a unique model, so repeat constant step three and select the model which provides the most effective result as per the business demand.

4. Deploying Models:- Once we have a tendency to get the required result by correct testing as per the business necessities, we have a tendency to end the model, which provides America the most effective result as per testing results, and deploys the model within the production surroundings.

Data Science Program Advantage:

1. easy is the best:- Deep learning models with several layers may appear charming to you however most of the time, they're a lot of advancer than necessary. It's continually fascinating to accomplish the task with a less complicated model. A picture recognition task might need a deep learning model however we tend to don’t want that quantity of complexness for churn prediction. The business necessities conjointly play an essential role here. If ninetieth accuracy satisfies the wants, there's no want for extra completeness. As models get a lot of advanced, they need a lot of computation power which implies extra prices. Moreover, deep learning models as data-hungry. We want to feed them AN excess quantity of information to get AN correct and not-overfitting model. Each information and computation power mean value therefore easy is the best once applicable.

2. Algebra will The Magic:- Linear algebra is just like the hero behind the scenes. Information comes in many various formats like numbers, texts, images, sound waves. However, the information should be born-again to numbers for a model to form sense of it. The information is in tabular format (i.e. rows and columns) that is depicted with matrices. Thus, immeasurable computations as done throughout the coaching of a model and this is often wherever algebra comes into play. These computations a supported matrix or vector operations that a at the core of algebra. Once the gradient descent rule works its manner through the model convergence, matrix/vector computations as done beneath the hood. We tend to don’t see them. We tend to as a lot of fascinated by the top result that is that the accuracy or loss. However, to require our understanding one step any and transcend accuracy and loss, we want to possess a comprehensive understanding of algebra ideas. It should be boring initially however gets exciting once you get a suspend of it. I powerfully recommend economical time to find out algebra as a result of it's one among the basics in your information science journey.

3. Domain data is vital to Success:- Data science applications a designed to resolve issues or improve processes. For example, machine learning will be applied to form wise business choices. But, what a those issues and processes? What's the aim of that call that we tend to aim to use machine learning techniques? We need to possess domain data to answer these queries. We tend to be also excellent at information preprocessing, feature engineering, or model choosing and implementation. However, our skillset is a lot of complete with domain data. for instance, if we tend to as building a machine learning product to be employed in boring, we want to grasp the small print of the fossil fuel distillation method. What quite information is collected and what they mean a vital queries that reveal valuable data for our model. I’m not spoken communication we must always be AN skilled in boring however it's completely helpful to find out the dynamics of processes. Domain data helps North American nation in information preprocessing steps additionally. we are able to create wise choices once handling missing values. Correlations among bound options create a lot of sense if we've got AN understanding of underlying processes.

4. Do comes:- Data science journey starts with learning the fundamentals. There a immeasurable nice sources to urge you started. Introduction to information science courses helps to urge aware of the sphere. a lot of sensible courses might target exploitation specific tools or packages like TensorFlow or PyTorch. The documentation of open supply packages is additionally terribly informative. Reckoning on your background, you will get to take some programming or package categories additionally. Once the fundamentals as complete. it's time to begin doing comes. What we tend to learn from comes aren't delimited with bound topics or tools. But, the foremost valuable ability to find out from comes is that the ability to properly approach a retardant. however we tend to approach a retardant is vital to make a sturdy and valuable model or product. Doing immeasurable comes in several areas won't solely improve our sensible skills however conjointly enhance our manner of thinking as a knowledge person.

5. Optimisation Algorithms Matter loads:- We take the data. Clean it. Apply information preprocessing and have engineering techniques. Produce a model. Then train the model with information we tend to ready. Models a trained to see model parameters in order that relationships between options and target a mapped as correct as doable. The method of learning the parameters is termed optimisation and therefore the techniques employed in this process as optimisation algorithms. For example, random gradient descent may be a wide used optimisation rule in machine learning and deep learning. A comprehensive understanding of optimisation algorithms facilitate to raised interpret the models. we are able to create a lot of sense of the accuracy and different performance metrics. Thus, we'll be ready to improve our model in a very structured manner.

Data Science Developer job Responsibilities:

Data scientists work closely with business stakeholders to grasp their goals and verify however information will be wont to attain those goals. They style information modeling processes, produce algorithms and prognostic models to extract the information the business desires, and facilitate analyze the information and share insights with peers. Whereas every project is totally different, the method for gathering and analyzing information usually follows the below path:

1. Raise the proper inquiries to begin the invention method 2. Acquire information 3. Method and clean the information 4. Integrate and store information 5. Initial information investigation and explorative information analysis 6. opt for one or a lot of potential models and algorithms 7. Apply information science techniques, like machine learning, applied mathematics modeling, and computer science 8. live and improve results 9. gift ending to stakeholders 10. create changes supported feedback 11. Repeat the method to resolve a replacement downside

Pay Scale of Data Science Developer:

According to Linkedin, the typical information scientist's regular payment is 8,50,000. A mid-level information person will earn around 10,00,000 each year with five to eight years of expertise. Early-level information scientists with one to two years of expertise get around 6,11,000 each year.

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Key Features

ACTE Chandigarh offers Data Science Training in more than 27+ branches with expert trainers. Here are the key features,
  • 40 Hours Course Duration
  • 100% Job Oriented Training
  • Industry Expert Faculties
  • Free Demo Class Available
  • Completed 500+ Batches
  • Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.


Syllabus of Data Science Course in Chandigarh
Module 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.
Module 2: Data Exploration
  • 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.
Module 3: Data Manipulation
  • 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.
Module 4: Data Visualization
  • 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.
Module 5: Introduction to Statistics
  • 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.
Module 6: Machine Learning
  • 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.
Module 7: Logistic Regression
  • 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.
Module 8: Decision Trees & Random Forest
  • 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.
Module 9: Unsupervised learning
  • 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.
Module 10: Association Rule Mining & Recommendation Engine
  • 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.
Module 11: Introduction to Artificial Intelligence (self paced)
  • 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.
Module 12: Time Series Analysis (self paced)
  • 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.
Module 13: Support Vector Machine - (SVM) (self paced)
  • Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.
Module 14: Naïve Bayes (self paced)
  • 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.
Module 15: Text Mining (self paced)
  • 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.
Module 16: Case Study
  • 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.
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Hands-on Real Time Data Science Projects

Project 1
Breast Cancer Classification Project

The main objective of this manuscript is to report on a research project where we took advantage of those available technological advancements to develop prediction models.

Project 2
IMDB Predictions Project

Our object is to build a system to predict IMDB users rating about movies with different algorithms and compare their performance through a benchmark.

Project 3
Image Caption Generator Project

Image caption generator is a task that involves computer vision and natural language processing concepts to recognize the context of an image and describe them.

Project 4
Wallmart Sales Data Set

The objective of the project is to build an application that could predict the sales using the Walmart dataset. This application will help in providing us with the data company.

Our Esteemed Placement Partners

ACTE Chandigarh is certify around the world. It expands the worth of your resume and you can accomplish driving position posts with the assistance of this affirmation in driving MNC's of the world. The certificate is just given after fruitful finishing of our preparation and pragmatic based undertakings.
  • Our placement team identifying the requirements and assumptions for the organizations to help them in enlisting most appropriate competitors.
  • To help applicants to create/explain their scholastic and vocation interests, and their short and long haul through singular guiding and group dicussions.
  • Top Tech Giants like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM, etc have a partnership with us which makes it possible to place our students in top MNCs.
  • We Coordinating with organizations to find out about their prerequisites and enrollment strategies and Social affair data about work fairs and all pertinent enlistment promotions.
  • Fixing up a commonly advantageous date of the drive, advising understudies about the organization and expected set of responsibilities through different methods for correspondence like ACTE Site and Facebook, Messages, Mass messages, Whatsapps and individual declarations.
  • Invite organizations for entry level positions and situations at grounds The training primarily focuses on case studies and project work on the technology which enables a candidate to understand the industrial orientation of the technology.

Get Certified By MCSE: Data Management and Analytics & Industry Recognized ACTE Certificate

Acte Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees. Our certification at Acte is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.

Complete Your Course

a downloadable Certificate in PDF format, immediately available to you when you complete your Course

Get Certified

a physical version of your officially branded and security-marked Certificate.

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About Adequate Data Science Instructor

  • Our Data Science Training in Chandigarh. Guide understudies to pick right vocation and to give information, ability, and inclination and meet the labor prerequisites of the Business.
  • Their expertise skills help to identify the need of participants for effective Data Science Trainers and are committed to the choicest quality of learning and development that ensures a positive experience.
  • Mentors give industry openness to the applicants by organizing successive modern visits and leading workshops and classes by industry specialists.
  • Train the understudies all year on employability abilities needed in the work market. Tutor and insight the applicants and place them in Top Mncs.
  • Our Mentor keeping up relationship with our understudies who are dominating in their callings and are standing firm on capable footings in driving organizations across the globe.
  • Many IT industries and firms have recognized us with various respectable awards for our Best Data Science Training.

Data Science Course Reviews

Our ACTE Chandigarh Reviews are listed here. Reviews of our students who completed their training with us and left their reviews in public portals and our primary website of ACTE & Video Reviews.



ACTE is the best training institute for Data science and Data Analytics in BTM Layout. 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.


Software Engineer

ACTE Institute best place to learn new technologies in Chandigarh, trainer raja is very kind and patience trainer resolves all your queries without any hesitation in Data Science. Thanks..


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 BTM Layout.



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.


Software Engineer

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.

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Data Science Course FAQs

Looking for better Discount Price?

Call now: +91 93833 99991 and know the exciting offers available for you!
  • 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
  • Our Placement Cell support you till you get placed in better MNC
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
    • Gives
    • For Completing A Course
  • Certification is Accredited by all major Global Companies
  • ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore
  • 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
All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

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
Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.
We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Data Science batch to 5 or 6 members
Our courseware is designed to give a hands-on approach to the students in Data Science. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
You can contact our support number at +91 93800 99996 / Directly can do by's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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