Data Science Course In Chennai – 100% Job Placement
- Live Project
- 100% Placement Support
- Affordable Fees
- Global Certification
- Real-Time Expert Trainers
- Time-flexibility
Ficusoft Technologies offers You the100% placement and real-time projects guided by our experienced trainers to meet the needs of the Students.
Ficusoft provides you with the training with flexible time and affordable cost. Upgrade your Data Science capabilities and skills to get a job in the field.
Overview - Data Science Course In Chennai
Real-Time Experts as Trainers
Live Projects
Real-Time Experts as Trainers
Who Are Data Scientists?
Certifiation
Affordable Fees
Live Projects
What Is Data Science?
Flexibility
Placement Support
Certifiation
Why Learn Data Science?
1. Data Science is a vast field with the potential to create new industries and revolutionize existing ones.
2. The demand for qualified Data Scientists is rising rapidly, but there aren’t enough people with the skills. Data Science course in Chennai…
3. The demand for qualified Data Scientists is rising rapidly, but there aren’t enough people with the skills.
4. It’s never been easier to learn data science than it is today! Data science course in Chennai…
5. Data Scientist is one of the highest-paying jobs today, with demand only continuing to rise!
6. Data Science is a highly-in-demand skill! Companies are desperate for skilled individuals who can solve their data-related problems.
7. Ficusoft Technologies offers 100% placement and real-time projects guided by our experienced trainers to meet the needs of the Students.
8. Ficusoft provides you with training with flexible time and affordable cost. Data science course in Chennai…
Upgrade your Data Science capabilities and skills to get a job in the field. Come to us at Ficusoft Technologies (the institute) and learn from experts from various fields.
Affordable Fees
Types Of Data Science
There are numerous varieties of data science that are as follows:
1. Computational Statistics: includes the use of statistical methods to solve problems that involve intensive computation, especially problems that cannot be solved on a computer otherwise.
2. Machine Learning: can be described as a set of techniques for making predictions from data and for finding patterns in large sets of data or data streams. Data Science c in Course in Chennai.
Also, including classification tasks (deciding what category something belongs to), clustering tasks (grouping things into related categories), and regression tasks (predicting how likely something is to occur).
3. Artificial Intelligence: involves the creation and study of computer systems with properties.
Also, that resemble those traditionally ascribed to human intelligence, such as problem-solving ability, knowledge representation, learning and adaptation, and natural language processing.
4. Predictive Analytics: it refers to the process of analyzing current and historical information about individuals and entities.
Usually big data sets such as customer transactions, store purchases, social media interactions, etc.,
Using predictive models which have been derived using machine learning algorithms.
The most common type is predictive modelling which seeks to find correlations among various features within a data set. Data science course in Chennai…
5. Social Media Analytics: includes analytics involving the collection, analysis, and use of data from social networks, blogs, forums, etc. for purposes such as research, marketing, or relationship building.
We at Ficusoft provide you with extensive hands-on practical training experience along with 100% placement assistance in Data Science Course in Chennai at Ficusoft to meet your needs!
Flexibility
Main Components Of Data Science
Data Science is a new field of study that has emerged due to the recent exponential growth of Data.
Data Science deals with extracting value from data and using it to create predictive models, segmentation, and other analytical products.
It can be applied to any industry but is most common in finance, marketing, retail, healthcare, and sport. Data science course in Chennai…
The four main components of Data Science are:
1) data acquisition: acquiring large datasets.
2) data cleaning: cleaning datasets of errors and inconsistencies.
3) data modelling: performing mathematical operations on datasets to analyze and transform them into understandable formats.
4) communicating results: Data Science is also commonly used synonymously with predictive analytics, but predictive analytics actually encompasses only a small subset of Data Science.
Predictive modelling isn’t concerned with cleaning or communicating results; instead, it focuses on making predictions about future behaviour based on past data.
It’s often used for marketing applications and by companies that deal with large datasets. Data science course in Chennai…
Placement Support
Benefits Of Data Science
1. Data Science is an emerging field that promises to transform the way we live, work, and interact. Data science course in Chennai.
2. Data Science is a multi-disciplinary field, which requires expertise from many different domains including Statistics, Mathematics, Computer Science, and Engineering.
3. It provides a set of powerful tools to extract knowledge and insights from data in order to identify patterns and trends that can be used for decision-making purposes.
4. It has gained traction as a result of Big Data, which is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications.
5. Data Science uses data mining, statistics, and machine learning algorithms to find patterns and generate models that make accurate predictions. Data Science Course In Chennai…
6. The emphasis on big data emphasizes its importance in areas like medicine, education, business intelligence, and fraud detection.
7. For example, some companies have started using Artificial Intelligence (AI) or Machine Learning (ML) techniques to predict customer behaviour with 80% accuracy levels or higher.
Why Choose Ficusoft To Learn About Data Science?
How To Be Certified In Data Science?
Career Options After Completing Data Science
- After completing a data science course, you will be able to find a job as a Data Scientist or Data Engineer. You can also do the following…
- Become an analytics consultant.
Pursue an advanced degree in data analytics or data engineering. - Become a business intelligence analyst. Data science course in Chennai.
Become an associate professor at a higher education institution or university. - Explore options in entrepreneurship and work on their own start-up ventures. Data science course in Chennai.
- Become a statistician with the U.S. Census Bureau, Bureau of Labor Statistics, or other research organizations that conduct statistical analysis on behalf of governments and businesses.
- Work for data visualization and data communication companies, such as Tableau, IBM Watson Analytics, Zillow Group’s Zillow Research Team, and Domo.
- Join the ranks of machine learning engineers (ML) who are responsible for developing the algorithms that power applications like Apple Siri and Google Now.
- Explore careers in academia at colleges/universities where they may teach courses on quantitative subjects related to statistics and math. Data science course in Chennai…
- Provide predictive analytics services to help businesses develop efficient marketing strategies based on past consumer behaviour.
Frequently Asked Questions
1. Why do I need Data Science skills?
Data Science skills are valuable for both employers and job seekers. If you have Data Science skills, your resume will stand out from all other candidates, so you’ll have a higher chance of landing your dream job.
Also if you have Data Science skills, it can help you get your next dream job with more salary and better perks. Data science course in Chennai.
2. What are the prerequisites for a Data Science course?
You need some basic knowledge of programming, databases, and statistics. It’s important to get these basics right before you apply for a Data Science job or pursue a full-time Data Science career.
At Ficusoft Technologies, we help students acquire these prerequisites and make them skilled enough to get on-the-job training and become certified professionals with ease. Data science course in Chennai.
3. How much time does it take to master Data Science skills?
It depends on how fast you learn! If you have zero experience or just want to refresh your existing data science knowledge, then it will take anywhere between 2 weeks to 6 months based on your learning speed.
4. How can I get support from our trainers?
You can contact our representatives via email, phone calls, and live chat. Any questions you may have will be answered by us with pleasure. Data science course in Chennai.
5. Do I need any previous data science background?
No previous background is required. Our expert instructors teach you everything that you need to know to start your first project.
Ficusoft offers 100% placement and real-time projects guided by our experienced trainers to meet the needs of the Students. Contact us now to know more about the course details
Data Science Course Syllabus
What Is Data Science ?
Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.
Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover solutions to business challenges.
What Is Data Science ?
Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.
Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover solutions to business challenges.
What Is Data Scientist ?
Components Of Data Science
Data (and Its Various Types)
The raw dataset is the foundation of Data Science, and it can be of various types like structured data (mostly in a tabular form) and unstructured data (images, videos, emails, PDF files, etc.)
Programming (Python and R)
Data management and analysis is done by computer programming. In Data Science, two programming languages are most popular: Python and R.
Statistics and Probability
Data is manipulated to extract information out of it. The mathematical foundation of Data Science is statistics and probability. Without having a clear knowledge of statistics and probability, there is a high possibility of misinterpreting data and reaching at incorrect conclusions. That’s the reason why statistics and probability play a crucial role in Data Science.
Skills required to become a data scientist include:
Techniques
⦁ Mathematical Expertise: Data scientists also work on machine learning algorithms such as regression, clustering, time series etc which require a very high amount of mathematical knowledge since they themselves are based on mathematical algorithms.
⦁ Working with unstructured data: Since most of the data produced every day, in the form of images, comments, tweets, search history etc is unstructured, it is a very useful skill in today’s market to know how to convert this unstructured into a structured form and then working with them.
Job Trends
Jobs by Salary
Nearly 46% of Data Scientists earn a salary between 6-15 LPA.
Python – Programming Syllabus
The Syllabus of will be framed as per the requirement of corporate. We are mainly concentrating in the following.
Introduction to Python
⦁ Overview of Python- Starting with Python
⦁ Introduction to installation of Python
⦁ Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
⦁ Understand Jupyter notebook & Customize Settings
⦁ Concept of Packages/Libraries – Important packages(NumPy,SciPy,scikit-learn,Pandas,Matplotlib,etc)
⦁ Installing & loading Packages & Name Spaces
⦁ Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
⦁ List and Dictionary Comprehensions
⦁ Variable & Value Labels – Date & Time Values
⦁ Basic Operations – Mathematical – string – date
⦁ Reading and writing data
⦁ Simple plotting
⦁ Control flow & conditional statements
⦁ Debugging & Code profiling
Scientific distributions used in python for Data Science
⦁ Numpy, scify, pandas, scikitlearn etc
Accessing/Importing and Exporting Data using python modules
⦁ Importing Data from various sources (Csv, txt, excel, access etc)
⦁ Database Input (Connecting to database)
⦁ Viewing Data objects – subsetting, methods
⦁ Exporting Data to various formats
⦁ Important python modules: Pandas
Data Manipulation – cleansing – Munging using Python modules
⦁ Cleansing Data with Python
⦁ Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables,
⦁ sampling, Data type conversions, renaming, formatting etc)
⦁ Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
⦁ Python Built-in Functions (Text, numeric, date, utility functions)
⦁ Python User Defined Functions
⦁ Stripping out extraneous information
⦁ Normalizing data
⦁ Formatting data
⦁ Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
Data Analysis – Visualization using Python
⦁ Introduction exploratory data analysis
⦁ Descriptive statistics, Frequency Tables and summarization
⦁ Univariate Analysis (Distribution of data & Graphical Analysis)
⦁ Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
⦁ Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
⦁ Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)
Basic statistics & implementation of stats methods in Python
⦁ Basic Statistics – Measures of Central Tendencies and Variance
⦁ Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
⦁ Inferential Statistics -Sampling – Concept of Hypothesis Testing
⦁ Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square
⦁ Important modules for statistical methods: Numpy, Scipy, Pandas
Machine Learning
Predictive Modeling – Basics
⦁ Introduction to Machine Learning & Predictive Modeling
⦁ Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs.
Forecasting
⦁ Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
⦁ Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
Machine Learning Algorithms & Applications – Implementation in Python
⦁ Linear Regression
⦁ Segmentation – Cluster Analysis (K-Means)
⦁ Decision Trees (CART/CD 5.0)
⦁ Support Vector Machines(SVM)
⦁ Other Techniques (KNN, Naïve Bayes, )
⦁ Important python modules for Machine Learning (SciKit Learn, scipy, etc)
Deep Learning
⦁ Artificial Neural Networks(ANN)
Deep Learning Course Syllabus
Convolutional Neural Networks
⦁ Invariance, stability.
⦁ Variability models (deformation model, stochastic model).
⦁ Scattering networks
⦁ Group Formalism
⦁ Supervised Learning: classification.
⦁ Properties of CNN representations: invertibility, stability, invariance.
⦁ covariance/invariance: capsules and related models.
⦁ Connections with other models: dictionary learning, LISTA.
⦁ Other tasks: localization, regression.
⦁ Embeddings (DrLim), inverse problems
⦁ Extensions to non-euclidean domains
⦁ Dynamical systems: RNNs.
⦁ Guest Lecture
Deep Unsupervised Learning
⦁ Autoencoders (standard, denoising, contractive, etc etc)
⦁ Variational Autoencoders
⦁ Adversarial Generative Networks
⦁ Maximum Entropy Distributions
Miscellaneous Topics
⦁ Non-convex optimization for deep networks
⦁ Stochastic Optimization
⦁ Attention and Memory Models
⦁ Open Problems
Artificial Intelligence Course Syllabus
Artificial Intelligence is a changing technology and hence the syllabus is updated such that we deliver the latest and best. Algorithms and data are the key to artificial intelligence and we teach you the importance of them.
⦁ Artificial intelligence fundamentals
⦁ Computational mathematics for learning and data analysis
⦁ Machine learning
⦁ Human language technologies
⦁ Parallel and distributed systems: paradigms and models
⦁ Intelligent Systems for pattern recognition
⦁ Algorithm engineering (KD)
⦁ Data mining (KD)
⦁ Mobile and cyber-physical systems (ICT)
⦁ Real-time Data Warehouse migration
⦁ Information retrieval (KD)
⦁ Social and ethical issues in computer technology
⦁ Computational neuroscience (ING)
⦁ Robotics
⦁ Semantic web