About Data Science Course
Key highlights of our course:
- Modules made more useful with practical assignments.
- Besant’s instructors are top IT experts having 10+ years of experience.
- We offer 24×7 support.
- We offer live online training.
- You can also learn from easily accessible recorded training sessions.
- You get full lab support. Need a software for learning? Come to us.
- Enrolment costs are reasonable.
- Class timings are flexible.
- Placement guidance guaranteed.
- Live demos as per the demand of the course.
- Most importantly, we offer a course completion certificate that’s globally valid!
What is Data Science? What learning outcomes can you expect from your training at Besant Technologies?
Data Science is an interdisciplinary approach to deriving insights from structured and unstructured data. Various processes, systems, algorithms and scientific methods are used for the extraction of meaningful insights out of random data.
There are numerous reasons for you to take our Data Science Course in Chennai, but we recommend this course for the diverse career avenues it can open up for anyone. What learning outcomes can you expect from this course? Read on to find out.
The learning outcomes of Data Science Training in Chennai at Besant:
- Besant’s Data Science Course offers hands-on experience in extracting, processing and interpreting data.
- You will understand the general importance of data and its significance from a business standpoint.
- We have a systemically designed and extremely focused curriculum that prepares you for internships and full-time placements with reputed MNCs.
- Our modules are targeted at developing advanced data skillsets in students that are extremely necessary for solving various prevalent problems.
- You will be able to use appropriate tools and techniques for tackling real world data challenges.
- You will be thorough with both principles and practical applicability of Data Science as a field of knowledge.
- You will also be able to apply algorithms for machine learning.
Advantages of Data Science Certification Course in Chennai at Besant:
- With our certification course, you will be eligible for diverse job opportunities in the field of Data Science.
- Companies utilize data in various ways. By being a certified Data Science expert, you can be a valuable asset for businesses.
- Data Science salaries are one of the highest in the IT world. By choosing to take Besant’s globally recognized Data Science Training in Chennai, you make yourself apt for such high paying jobs.
- Our certification course keeps up your competitive advantage by helping you get noticed by big hiring companies.
- Your learnings from the course are validated by the course completion certificate which earns you global appeal as a job candidate.
Who can enroll for Besant’s Data Science Certification Course in Chennai?
If you have the desire to join our course, you can join it regardless of your educational background.
You don’t need to have any specialized IT knowledge to join our Data Science Course in Chennai. However, there are many global certification exams that require you to have specific educational qualifications.
The most popular certifications for Data Science:
Data Science Council of America (DASCA), Principle Data Scientist (PDS).
There are four tracks and each track requires professional experience in the capacity of Data Scientist for at least 10 years. The tracks involve high-level topics in technology and analysis. Three tracks out of four require a degree in some master’s program. The certification is valid for a lifetime, but each track will cost more than a lot of other exams.
Certified Analytics Professional (CAP).
With this certification, you get valuable big data insights. To earn a CAP certification, it’s required that you clear the Associate test. INFORMS members receive a discount on the certification fee. The validity of this certification is 3 years.
Open Certified Data Scientist (Open CDS)
You can get this one via board review and application, rather than traditional coursework or exams. As you move through its different certification levels which are three in total, you gain experience which gets reflected in your certification. The certification’s validity is for a lifetime.
Data Science Council of America (DASCA), Senior Data Scientist (SDS).
There are five tracks to this certification. Each track demands different experience. The 1st track requires a minimum of 5 years of Data Science professional experience and a bachelor’s degree. Remaining tracks need you to have a master’s in addition to other certifications. Make sure you check the requirements of each program. This program is multifaceted in nature. The validity of DASCA’s certification is 5 years.
SAS Certified Big Data Professional
This program requires participants to have some programming experience in SAS or some other language. This certification covers nine courses that will cover basic statistics, data analysis, communication and programming and data tools. It is valid for a lifetime.
SAS Certified Advanced Analytics Professional
With this certification, you will know a lot about predictive modeling, data optimization etc. To earn the certification, you must have done nine courses besides clearing three exams. A minimum of 6 months of experience is required in SAS or some other language. If you have a minimum of 6 months worth experience in Data Science for businesses, then it’s highly recommended that you do this course. This certification has a lifetime validity.
SAS Certified Data Scientist
The SAS certifications you have read about above combine with this course to earn you an extra distinction. To be eligible for this program, you must have cleared the earlier courses. It teaches improvement and management of data as well as its manipulation and access. Additionally, you will master using tools for data visualization. The validity of the certificate is for a lifetime.
Google Data Machine Learning
Mastery in three tracks are required to obtain this certification. They all focus heavily on using Google Cloud for analyzing data. There are also modules that focus on machine learning and data engineering. The certification’s validity lasts for lifetime.
Google Certified Professional Data Engineer
This exam is for candidates that are familiar with GCP or Google’s cloud platform. It tests a candidate’s knowledge in this area. Your understanding of the models of machine learning starting from design through security and operation will be assessed. This certification does not expire.
Cloudera Certified Associate Data Analyst
The test assesses your knowledge of the enterprise software by Cloudera. This exam also tests your knowledge in programming, analyzing and administering on Cloudera. All these skills are essential for Data Scientists, making this certification useful for beginners. The certification has a validity of two years.
IBM Data Science Professional Certificate
It is for nine courses and covers areas like a capstone in applied Data Science, machine learning, SQL, Python and databases, data analysis and visualization etc.
Register for a subscription online to complete the program according to your convenience. Once you have completed the training, you can include the project portfolio in your resume. It’s however recommended that you complete the program in three months. The certification is valid for life.
Cloudera Certified Professional: CCP Data Engineer
This exam is only available for those who have cleared the Associate test. This is a high- level, performance-oriented certification which needs you to have deep knowledge of data engineering. Data Scientists that develop and design for production ecosystems benefit immensely from this course. The test lasts for 4 hours and requires you to resolve a variety of customer problems with the help of Cloudera Enterprise cluster. The certification expires in 3 years.
Dell EMC Data Science Track
The training to earn this certification takes place in 2 levels. You first receive the associate certification in Data Science basics and big data analysis. The second program covers platforms such as Hive, natural language processing, Hadoop and HBase, visualization methods and more.
Microsoft Certified Solutions Expert (MCSE): Data Management and Analytics
Microsoft offers a wide range of specialties and IT skills, including this certification. The tracks on Data Science include data analysis, management, and its applications in businesses. The certification involves rigorous assessment. And it has a validity of 3 years.
Microsoft Certified Azure Data Scientist Associate
For this certification, you must have expertise in applying Data Science to run and implement machine learning with the help of Azure. You need to have experience with Data Science, Azure Data bricks and Azure Machine Learning. This certification is valid for a lifetime.
Oracle Business Intelligence
Many popular computer languages and database software known to the world such as Java and MySQL are owned by Oracle. Oracle’s BI tools are in use in many big companies, so becoming certified in this course can add value to your skills. Oracle offers several in-person and online tests to offer relevant certifications.
Institute for Operations Research and the Management Sciences Certified Analytics Professional (CAP).
This certification involves training in the development of analytical methods, model building, lifecycle management, and analytic problems. It also provides data acquisition and implementation. The exam doesn’t focus on one platform, it covers all aspects of Data Science. A degree in some bachelor’s program is required, along with a minimum of 3 years of analytics experience. INFORMS requires that candidates verify their skills by their employer.
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Syllabus of Data Science Course in Chennai
Data Science with Python
Module 1: Introduction to Data Science (Duration-1hr)
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & its types
Module 2: Introduction to Python (Duration-1hr)
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
- Installing Python idle for windows,Linux and
- Creating “Hello World” code
Module 3: Python Basics (Duration-5hrs)
- Python Basic Data types
- IF statements
- Selection by position & Labels
- Practice and Quickly learn Python necessary skills by solving simple questions and problems.
- how Python uses indentation to structure a program, and how to avoid some common indentation errors.
- You executed to make simple numerical lists, as well as a few operations you can perform on numerical lists, tuple, dictionary and set
Module 4: Python Packages (Duration-2hrs)
- Sci-kit Learn
- Mat-plot library
- Installing jupyter notebook for windows, Linux and
- Installing numpy, pandas and matplotlib
Module 5: Importing Data (Duration-1hr)
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to CSV file
- To generate data sets and create visualizations of that data. You learned to create simple plots with matplotlib, and you saw how to use a scatter plot to explore random
- You learned to create a histogram with Pygal and how to use a histogram to explore the results of rolling dice of different
- Generating your own data sets with code is an interesting and powerful way to model and explore a wide variety of real-world
- As you continue to work through the data visualization projects that follow, keep an eye out for situations you might be able to model with
Module 6: Manipulating Data (Duration-1hr)
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
- As you gain experience with CSV and JSON files, you’ll be able to process almost any data you want to analyze.
- Most online data sets can be downloaded in either or both of these From working with these formats, you’ll be able to learn other data formats as well.
Module 7: Statistics Basics (Duration-11hrs)
- Central Tendency
- Normal Distribution
- Probability Basics
- What does it mean by probability?
- Types of Probability
- ODDS Ratio?
- Standard Deviation
- Data deviation & distribution
- Bias variance Tradeoff
- Distance metrics
- Euclidean Distance
- Manhattan Distance
- Outlier analysis
- What is an Outlier?
- Inter Quartile Range
- Box & whisker plot
- Upper Whisker
- Lower Whisker
- Scatter plot
- Cook’s Distance
- Missing Value treatment
- What is NA?
- Central Imputation
- KNN imputation
- Pearson correlation
- positive & Negative correlation
- Compute probability in a situation where there are equally-likely outcomes
- Apply concepts to cards and dice
- Compute the probability of two independent events both occurring
- Compute the probability of either of two independent events occurring
- Do problems that involve conditional probabilities
- Calculate the probability of two independent events occurring
- List all permutations and combinations
- Apply formulas for permutations and combinations
Module 8: Error Metrics
- Confusion Matrix
- F1 Score
- State why the z’ transformation is necessary
- Compute the standard error of z
- Compute a confidence interval on ρ The computation of a confidence interval
- Estimate the population proportion from sample proportions
- Apply the correction for continuity
- Linear Regression
- Linear Equation
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure Bias Variance Tradeoff
- ROC curve
- Bias Variance Tradeoff
- we’ve reviewed the main ways to approach the problem of modeling data using simple and definite
Unsupervised Learning (Duration-4hrs)
- K-Means ++
- Hierarchical Clustering
- Support Vectors
- 2-D Case
- Linear Hyperplane
SVM Kernal (Duration-2hrs)
Other Machine Learning algorithms (Duration-10hrs)
- K – Nearest Neighbour
- Naïve Bayes Classifier
- Decision Tree – CART
- Decision Tree – C50
- Random Forest
- We have covered the simplest but still very practical machine learning models in an eminently practical way to get us started on the complexity
- where we will cover several regression techniques, it will be time to go and solve a new type of problem that we have not worked on, even if it’s possible to solve the problem with clustering methods (regression), using new mathematical tools for approximating unknown values.
- In it, we will model past data using mathematical functions, and try to model new output based on those modeling
Module 1: AI Introduction (Duration-9hrs)
- Multi-Layer perceptron
- Markov Decision Process
- Logical Agent & First Order Logic
- AL Applications
Module 1: Deep Learning Algorithms (Duration-10hrs)
- CNN – Convolutional Neural Network
- RNN – Recurrent Neural Network
- ANN – Artificial Neural Network
- We took a very important step towards solving complex problems together by means of implementing our first neural
- Now, the following architectures will have familiar elements, and we will be able to extrapolate the knowledge acquired on this chapter, to novel
Introduction to NLP (Duration-5hrs)
- Text Pre-processing
- Noise Removal
- Lexicon Normalization
- Object Standardization
Text to Features (Feature Engineering) (Duration-5hrs)
- Syntactical Parsing
- Dependency Grammar
- Part of Speech Tagging
- Entity Parsing
- Named Entity Recognition
- Topic Modelling
- TF – IDF
- Frequency / Density Features
- Word Embedding’s
Tasks of NLP (Duration-2hrs)
- Text Classification
- Text Matching
- Levenshtein Distance
- Phonetic Matching
- Flexible String Matching
- provided, you will even be able to create new customized
- As our models won’t be enough to solve very complex problems, in the following chapter, our scope will expand even more, adding the important dimension of time to the set of elements included in our generalization.
Project 1: Board Game Review Prediction
- To perform a Linear regression
- Analysis by predicting the average reviews in a board game
Project 2 :Credit Card Fraud Detection
- TO focus on Anomaly Detection by using probability densities to detect credit card fraud
Project 3: Stock Market Clustering
- Learn how to use the K-means clustering
- To find related companies by finding correlations among stock market movements over a given time span
Project 4: Getting Started with Natural Language Processing
- will focus on Natural Language Processing (NLP) methodology, such as tokenizing words
- and sentences, part of speech identification and tagging, and phrase
Project 5: Obtaining Near State-of-the-Art Performance on Object Recognition
- Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a
- benchmark to implement a recently published deep neural
Project 6: Image Super Resolution with the SRCNN – Learn how to implement & use
- Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for
- improving image
Project 7: Natural Language Processing: Text Classification
- an advanced approach to Natural Language Processing by solving a text classification task
- using multiple classification
Project 8: K-Means Clustering For Image Analysis
- use K-Means clustering in an unsupervised learning method to analyze and classify 28 x 28 pixel images from the MNIST
Project 9:Data Compression & Visualization Using Principal Component Analysis
- This project will show you how to compress our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering
Module 1: Tableau Course Material (Duration – 5 Hours)
- Start Page
- Show Me
- Connecting to Excel Files
- Connecting to Text Files
- Connect to Microsoft SQL Server
- Connecting to Microsoft Analysis Services
- Creating and Removing Hierarchies
- Joining Tables
- Data Blending
Module 2: Learn Tableau Basic Reports (Duration – 5 Hours)
- Grouping Example 1
- Grouping Example 2
- Edit Groups
- Combined Sets
- Creating a First Report
- Data Labels
- Create Folders
- Sorting Data
- Add Totals, Subtotals and Grand Totals to Report
- Install Tableau Desktop
- Connect Tableau to various Datasets: Excel and CSV files
Module 3: Learn Tableau Charts (Duration – 4 Hours)
- Area Chart
- Bar Chart
- Box Plot
- Bubble Chart
- Bump Chart
- Bullet Graph
- Circle Views
- Dual Combination Chart
- Dual Lines Chart
- Funnel Chart
- Traditional Funnel Charts
- Gantt Chart
- Grouped Bar or Side by Side Bars Chart
- Highlight Table
- Cumulative Histogram
- Line Chart
- Lollipop Chart
- Pareto Chart
- Pie Chart
- Scatter Plot
- Stacked Bar Chart
- Text Label
- Tree Map
- Word Cloud
- Waterfall Chart
- Create and use Static Sets
- Create and use Dynamic Sets
- Combine Sets into more Sets
- Use Sets as filters
- Create Sets via Formulas
- Control Sets with Parameters
- Control Reference Lines with Parameters
Module 4: Learn Tableau Advanced Reports (Duration – 6 Hours)
- Dual Axis Reports
- Blended Axis
- Individual Axis
- Add Reference Lines
- Reference Bands
- Reference Distributions
- Basic Maps
- Symbol Map
- Use Google Maps
- Mapbox Maps as a Background Map
- WMS Server Map as a Background Map
- Create Barcharts
- Create Area Charts
- Create Maps
- Create Interactive Dashboards
- Create Storylines
- Understand Types of Joins and how they work
- Work with Data Blending in Tableau
- Create Table Calculations
- Work with Parameters
- Create Dual Axis Charts
- Create Calculated Fields
Module 5: Learn Tableau Calculations & Filters (Duration – 6 Hours)
- Calculated Fields
- Basic Approach to Calculate Rank
- Advanced Approach to Calculate Ra
- Calculating Running Total
- Filters Introduction
- Quick Filters
- Filters on Dimensions
- Conditional Filters
- Top and Bottom Filters
- Filters on Measures
- Context Filters
- Slicing Fliters
- Data Source Filters
- Extract Filters
- Creating Data Extracts in Tableau
- Understand Aggregation, Granularity, and Level of Detail
- Adding Filters and Quick Filters
Module 6: Learn Tableau Dashboards (Duration – 4 Hours)
- Create a Dashboard
- Format Dashboard Layout
- Create a Device Preview of a Dashboard
- Create Filters on Dashboard
- Dashboard Objects
- Create a Story
Module 7: Server (Duration – 5 Hours)
- Tableau online.
- Overview of Tableau
- Publishing Tableau objects and scheduling/subscription.
- Create Data Hierarchies
- Adding Actions to Dashboards (filters & highlighting)
- Assigning Geographical Roles to Data Elements
- Advanced Data Preparation
Introduction to Oracle Database
- List the features of Oracle Database 11g
- Discuss the basic design, theoretical, and physical aspects of a relational database
- Categorize the different types of SQL statements
- Describe the data set used by the course
- Log on to the database using SQL Developer environment
- Save queries to files and use script files in SQL Developer
- Prepare your environment
- Work with Oracle database tools
- Understand and work with language features
Retrieve Data using the SQL SELECT Statement
- List the capabilities of SQL SELECT statements
- Generate a report of data from the output of a basic SELECT statement
- Select All Columns
- Select Specific Columns
- Use Column Heading Defaults
- Use Arithmetic Operators
- Understand Operator Precedence
- Learn the DESCRIBE command to display the table structure
- Individual statements in SQL scripts are commonly terminated by a line break (or carriage return) and a forward slash on the next line, instead of a semicolon.
- You can create a SELECT statement, terminate it with a line break, include a forward slash to execute the statement, and save it in a script file.
Learn to Restrict and Sort Data
- Write queries that contain a WHERE clause to limit the output retrieved
- List the comparison operators and logical operators that are used in a WHERE clause
- Describe the rules of precedence for comparison and logical operators
- Use character string literals in the WHERE clause
- Write queries that contain an ORDER BY clause to sort the output of a SELECT statement
- Sort output in descending and ascending order
- Creating the queries in a compound query must return the same number of columns.
- Create corresponding columns in each query must be of compatible data types.
- ORDER BY; it is, however, permissible to place a single ORDER BY clause at the end of the compound query
Usage of Single-Row Functions to Customize Output
- Describe the differences between single row and multiple row functions
- Manipulate strings with character function in the SELECT and WHERE clauses
- Manipulate numbers with the ROUND, TRUNC, and MOD functions
- Perform arithmetic with date data
- Manipulate dates with the DATE functions
- Create the distinction is made between single- row functions, which execute once for each
- row in a dataset, and multiple-row functions, which execute once for all the rows in a data- set.
Invoke Conversion Functions and Conditional Expressions
- Describe implicit and explicit data type conversion
- Use the TO_CHAR, TO_NUMBER, and TO_DATE conversion functions
- Nest multiple functions
- Apply the NVL, NULLIF, and COALESCE functions to data
- Use conditional IF THEN ELSE logic in a SELECT
- we create and discuss the NVL function, which provides a mechanism to convert null values into more arithmetic-friendly data values.
Aggregate Data Using the Group Functions
- Use the aggregation functions in SELECT statements to produce meaningful reports
- Divide the data into groups by using the GROUP BY clause
- Exclude groups of date by using the HAVING clause
- Group functions operate on aggregated data and return a single result per group.
- These groups usually consist of zero or more rows of data.
Display Data from Multiple Tables Using Joins
- Write SELECT statements to access data from more than one table
- View data that generally does not meet a join condition by using outer joins
- Join a table by using a self-join
Use Subqueries to Solve Queries
- Describe the types of problem that subqueries can solve
- Define sub-queries
- List the types of sub-queries
- Write a query that uses subqueries in the column projection list.
- Write single-row and multiple-row subqueries
The SET Operators
- Describe the SET operators
- Use a SET operator to combine multiple queries into a single query
- Control the order of rows returned
- Create The queries in the compound query must return the same number of columns.
- creating The corresponding columns must be of compatible data type.
- creating The set operators have equal precedence and will be applied in the order they are specified.
Data Manipulation Statements
- Describe each DML statement
- Insert rows into a table
- Change rows in a table by the UPDATE statement
- Delete rows from a table with the DELETE statement
- Save and discard changes with the COMMIT and ROLLBACK statements
- Explain read consistency
- Expressions and create expose a vista of data manipulation possibilities through the interaction of arithmetic and character operators with column or literal data, or a combination of the two.
Use of DDL Statements to Create and Manage Tables
- Categorize the main database objects
- Review the table structure
- List the data types available for columns
- Create a simple table
- Decipher how constraints can be created at table creation
- Describe how schema objects work
Other Schema Objects
- Create a simple and complex view
- Retrieve data from views
- Create, maintain, and use sequences
- Create and maintain indexes
- Create private and public synonyms
Control User Access
- Differentiate system privileges from object privileges
- Create Users
- Grant System Privileges
- Create and Grant Privileges to a Role
- Change Your Password
- Grant Object Privileges
- How to pass on privileges?
- Revoke Object Privileges
- create users and execute the
Management of Schema Objects
- Add, Modify and Drop a Column
- Add, Drop and Defer a Constraint
- How to enable and Disable a Constraint?
- Create and Remove Indexes
- Create a Function-Based Index
- Perform Flashback Operations
- Create an External Table by Using ORACLE_LOADER and by Using ORACLE_DATAPUMP
- Query External Tables
- Create the function based index and types.
Manage Objects with Data Dictionary Views
- Explain the data dictionary
- Use the Dictionary Views
- USER_OBJECTS and ALL_OBJECTS Views
- Table and Column Information
- Query the dictionary views for constraint information
- Query the dictionary views for view, sequence, index, and synonym information
- Add a comment to a table
- Query the dictionary views for comment information
Manipulate Large Data Sets
- Use Subqueries to Manipulate Data
- Retrieve Data Using a Subquery as Source
- Insert Using a Subquery as a Target
- Usage of the WITH CHECK OPTION Keyword on DML Statements
- List the types of Multitable INSERT Statements
- Use Multitable INSERT Statements
- Merge rows in a table
- Track Changes in Data over a period of time
Data Management in Different Time Zones
- Time Zones
- CURRENT_DATE, CURRENT_TIMESTAMP, and LOCALTIMESTAMP
- Compare Date and Time in a Session’s Time Zone
- DBTIMEZONE and SESSIONTIMEZONE
- Difference between DATE and TIMESTAMP
- INTERVAL Data Types
- Use EXTRACT, TZ_OFFSET, and FROM_TZ
- Invoke TO_TIMESTAMP, TO_YMINTERVAL and TO_DSINTERVAL
Retrieve Data Using Sub-queries
- Multiple-Column Subqueries
- Pairwise and Non Pairwise Comparison
- Scalar Subquery Expressions
- Solve problems with Correlated Subqueries
- Update and Delete Rows Using Correlated Subqueries
- The EXISTS and NOT EXISTS operators
- Invoke the WITH clause
- The Recursive WITH clause
Regular Expression Support
- Use the Regular Expressions Functions and Conditions in SQL
- Use Meta Characters with Regular Expressions
- Perform a Basic Search using the REGEXP_LIKE function
- Find patterns using the REGEXP_INSTR function
- Extract Substrings using the REGEXP_SUBSTR function
- Replace Patterns Using the REGEXP_REPLACE function
- Usage of Sub-Expressions with Regular Expression Support
- Implement the REGEXP_COUNT function
- Expressions and create the regular columns may be aliased using the AS keyword or by leaving a space between the column or expression and the alias. In this way, both wildcard symbols can be used as either specialized or regular characters in different segments of the same character string.
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Upcoming Batch Schedule for Data Science Training in Chennai
Besant Technologies provides flexible timings to all our students. Here are the Data Science Training Classes in Chennai Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.
- 04-12-2021Sat (Sat - Sun)Weekend Batch11:00 AM (IST) (Class 3Hrs) / Per SessionGet Fees
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Trainer Profile of Data Science Training in Chennai
Trainer Profile of Data Science Course in Chennai
We choose the best trainers for you. The best thing about taking Data Science Training in Chennai at Besant Technologies is certainly the trainers here. They are the best you can avail of and will take care of all your academic needs. The best thing about the approach with which they impart training to the candidates is that its student-centric. That means the training imparted to you is based on your needs, demands and comfort level. The teachers do their best to make sure their knowledge and wisdom in the field of data science get effortlessly passed on to you. They don’t just have immense theoretical knowledge but also a lot of practical experience to enrich you with.
- Our trainers are industry data experts who work in Data Science.
- Most of our trainers work in companies and have considerable work experience in Data science field.
- Our trainers are flexible and are available based on your timings
- Our trainers have good experience in training and are good at demonstrating the solution for real-world data science problems.
Data Science Exams & Certification
Besant Technologies 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 Besant Technologies 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.
Data Science Certification
Having a certificate for Data science will increase your placement chances in top companies. Candidates possessing data science certificates stand out from the crowd and have a greater probability of achieving their dream careers. There are many standard industry recognizable data scientist certification programs.
Advantages of Data Science Certification Courses in Chennai
There is no industry these days, that doesn’t deal with data. To be honest, data is everywhere. And to extract meanings out of so much of the data available at hand, you need data science knowledge. But where to get this knowledge from? Well, there is no dearth of Data Science Certification Courses these days. All you need is to select one as per your need and convenience and you are good to go. With a certification course in data science, you can make yourself eligible for any data science job available in the job market. This certification acts as a testimony to your knowledge of data science.
Key Features of Data Science Training in Chennai
30+ Hours Course Duration
100% Job Oriented Training
Industry Expert Faculties
Free Demo Class Available
Completed 800+ Batches
Projects of Data Science Training in Chennai
Our Live data science projects are designed based on the industry-related problems and simulate the working experience of data science fields.
Training Courses Reviews
I was a junior data scientist. It was my sheer interest in data science that compelled me to take up the data science training in Chennai at Besant Technologies. Since I already had a little knowledge of the subject, it was not really extremely difficult for me to have a grip on the course. But the training was imparted by the experienced teachers of Besant so effectively that even those fellow candidates who had no background in data science showed progress pretty fast. I benefitted a lot from this data science course in Chennai, and I can confidently say that I have developed a lot of understanding of the subject of data science through this training.
I always wanted to do something in the field of data science/Analytics. But I was not being able to find out a good training center. Then an ex Besant Technologies student told me about this institute. Besant Technologies has trained me so well in data science training in Chennai that I was reined in by the first company that interviewed me. I feel confident and blessed today. All thanks to Besant Technologies!
I never thought I could be a part of the IT world, given that I was not from a science background. Thankfully, Besant Technologies doesn’t need you to have any special educational background. The faculty members here taught us data science training in Chennai concepts so well, that never for a moment I felt I was no way related to technology. Now, I have joined an IT MNC. And working in a good position.
I am an ex Besant Technologies student. Although I liked everything about the coaching center, I especially loved the way the data science training in Chennai trainers imparts the course lessons. They not just emphasize theories but also practical. With regular practical training, I have learned a lot about the subject and its applicability. This has immensely helped in my job.
Being able to find a good data science institute from the crowd is indeed challenging. It took me a lot of time to zero in on one particular organization for data science training in Chennai. But then I am glad I chose Besant Technologies. It has a big contribution to whatever I have achieved in life.
I am inclined towards IT. And that is why I wanted to take data science training in Chennai from a good coaching center. When my friend told me about Besant, I did give it a second thought. But after some calculation, I went ahead and joined it. I feel thankful for myself that I did that. There is perhaps an institute that focuses so much on individual trainers’ learning and progress. Besant Technologies does, and that’s why it is different!