The recognized Data Science and Ai-based competence advanced certificate program offered by Cybernetics Guru’s Data Science department focuses on assisting you in mastering all the fundamental and advanced category skills that are essential in the fields of Data Science, Advanced Analytics, Machine – learning, and Cybernetics.Data Science Training at Cybernetics Guru, is the stupendous program containing a variety of Data Analytics and Data Science Training Techniques, This training is phenomenal in terms of content and delivery by world class faculty. This training masters important Data Science concepts such as Data Preprocessing, Exploratory Data Analytics, Data handling Techniques, Statistics, Algebra, maths, Machine Learning algorithms includes regression, classification and clustering. This course assists individuals to get ready by working on real-time-case studies and equipping them to work independently on relevant projects.
Lectures: 40-44, Duration: 100 hours
Introducing Data Science
Data science has some of the most sought-after occupations in computer programming and has essential relevance in most businesses. The investigators of such a big data era, data analysts are in charge of deducing important data findings from vast databases. The context of data science covers the full data process, much as a detective is accountable for gathering evidence, analyzing it, and finally presenting their legal case.
Starting with establishing and managing analytics solutions and database systems that effectively “clean” the information and give access to analytics at scale, this entails gathering a lot of raw data utilizing data collecting methods. Thanks to this analytics platform, data analysts can effectively process information by employing analysis and data modeling techniques and evaluate the results using cutting-edge methods, including predictive data and descriptive assessment.
Finally, these insights must be presented utilizing data visualization and reporting abilities to assist corporate decision-making. Data scientists would oversee the full data life cycle based on the scale of the organization, or they may focus on a specific stage of the product lifecycle as a member of a larger corporate team.
Who may submit a course application?
- People with a bachelor’s degree who are eager to learn about artificial intelligence and data science
- IT people interested in changing careers to become data scientists or AI engineers
- IT experts looking to advance their careers
- Business analysis and artificial intelligence experts
- Program managers and programmers
- Future professionals in the fields of computer science and data science who are recent graduates
What Are The Current Developments In Data Science Educational Courses?
Cybernetics Guru’s online learning is becoming more appealing as data science courses become more popular on campus. Many students who desire to enroll in these programs on campus discover that they are overenrolled or otherwise so packed that it is not easy to attend lectures and get in touch with teachers. Cybernetics Guru’s class recordings enable online learners to view lectures whenever they want in a concentrated setting, and digital office timings give frequent access to professors.
Thus, training can become more available to aspirant data scientists through online courses. The identity on your certificate is not something you have to give up just because you are studying online. Data science courses from prestigious universities, are now available through Cybernetics Guru.
What Activities Are Available for Data Science and Artificial Intelligence?
One of the trendiest jobs this year has been that of a data scientist. You are urged to perfect job-critical expertise such as statistics, testing of hypotheses, data analysis, and so much more by taking a Data Science certificate program jointly organized with Data Science.
1. Expert in data science
Recognize the problems, develop methods using the data acquired, and oversee a group of data scientists.
2. Expert in AI
Create strategies based on concepts and techniques to create Ai applications and advance the business.
3. Expert in computer science
Build predictive methods using huge amounts of company data and a variety of deep learning methods and equipment.
4. Multidisciplinary Scientist
Create and implement machine learning methods to generate intelligence for the group’s several more items and services.
5. Large Data Expert
Create and maintain bespoke pluggable service-based systems for data acquisition, cleansing, transformation, and validation.
6. Expert in Business Analysis
Execute industry analysis, retrieve information from the appropriate sources, and create reports, graphs, and analytics to track the firm’s success.
Additionally, the Cybernetics Guru Data Scientist course provides a thorough curriculum that combines instructor-led live sessions conducted online with self-paced training videos created in collaboration with Cybernetics Guru.
Why Should You Take Part in a Cybernetics Guru’s Data Science Course?
Students want to complete whole degree programs in digital marketing online in increasing numbers. For several reasons, starting with affordability, you may earn a degree from Cybernetics Guru at a lesser price than your on-campus peers while still receiving an excellent education.
Through internet streaming lectures and assignments, the Professional Certified Course in Data Science and AI program, taught by top professionals from Cybernetics Guru, would help you become adept in these subjects. Along with giving you professional experience in such fields through on-the-ground initiatives, they would assist you in gaining an in-depth understanding of Artificial Intelligence and Data Science.
You would receive an Advanced Certificate of completion in Machine Learning and Data Science from Cybernetics Guru, ensuing completing the program and graduating with the assigned tasks and projects. This certification would be acknowledged by prestigious organizations all around the globe. Our career aid staff would conduct numerous practice sessions, help you with your résumé, and other things to prepare you for your interview process.
-
Module 1-Introduction to Machine Learning with Python
-
Module 2 - Data Analysis with Python
-
Module 3 - Data Analysis with Numpy
-
Module 4 – Pandas and Advanced Analysis
- Introduction to Series
- Introduction to DataFrames
- Data manipulation with pandas
- Missing data
- Groupby
- Merging, joining and Concatenating
- Operations
- Data Input and Output
- Pandas in depth coding exercises
- Text data mining and processing
- Data mining applications in Data engineering
- POC – Analysis of e-commerce dataset using pandas
- POC – Getting insights on employee salaries data using data analysis in python
-
Module 5 – Data Visualization with Python
- Module 5.1- Matplotlib
- Plotting using Matplotlib
- Plotting Numpy arrays
- Plotting using object-oriented approach
- Subplots using matplotlib
- Matplotlib attributes and functions
- Matplotlib exercises
- Module 5.2- Seaborn Visualization
- Categorical Plot using Seaborn
- Distributional plots using Seaborn
- Matrix plots
- Grids
- Seaborn exercises
- Project – Getting insights using python analysis and visualizations on finance credit score data.
- Assignment – Pandas built-in data visualization Data visualization
-
Module 6 – Data Visualization with Tableau BI Tool
-
Module 7 – Mathematics and Statistics for Data Science
- Need of Mathematics for Data Science
- Exploratory data analysis (EDA)
- Numeric Variables
- Qualitative and Quantitative Analysis
- Types of Data Formats
- Measuring the Central Tendency – The Model
- Measuring Spread – Variance and Standard Deviation
- Euclidean Distance
- Confidence Coefficient
- Understanding Parametric Tests
-
Module 8 – Machine Learning Algorithms
- Introduction to Data Science
- Introduction to Artificial Intelligence
- Introduction to Machine Learning
- Need of Machine learning in forecasting
- Demand of forecasting analytics in current industrial trends
- Introduction to Machine Learning Algorithms Categories
- Introduction to Natural Language Processing (NLP)
- Introduction to Deep Learning
- Module 8.1 – Linear Regression with Python
- Introduction to Regression
- Exercise on Linear Regression using Scikit Learn Library
- Project on Linear regression using USA_HOUSING data
- Evaluation of Linear regression using python visualizations
- Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users.
- Module 8.2 – K- Nearest neighbours using Python
- Exercise on K-Nearest neighbors using Sci-kit Learn Library
- Project on Logistic regression using Dogs and horses’ dataset
- Getting the correct number of clusters
- Evaluation of model using confusion matrix and classification report
- Standard scaling problem
- Practice project on KNN algorithm
- Module 8.3 – Decision tree and Random forest with python
- Intuition behind Decision trees
- Implementation of decision tree using a real time dataset
- Ensemble learning
- Decision tree and random forest for regression
- Decision tree and random forest for classification
- Evaluation of the decision tree and random forest using different methods
- Practice project on decision tree and random forest using social network
- Data to predict if someone will purchase an item or not
- Module 8.4 – Support Vector Machines
- Linearly separable data
- Non-linearly separable data
- SVM project with telecom dataset to predict the users portability
- Module 8.5 – Principal Component Analysis
- Introduction to PCA
- Need for PCA
- Implementation to select a model on breast-cancer dataset
- Model evaluation
- Bias variance trade-off
- Accuracy paradox
- CAP curve and analysis
- Module 8.6 – Clustering in unsupervised learning
- K-means clustering intuition
- Implementation of K-means with Python using mall customers data to implement clusters on the basis of spending and income
- Hierarchical clustering intuition
- Implementation of Hierarchical clustering with python
- Module 8.7 – Association Algorithms
- A priori theory and explanation
- Market basket analysis
- Implementation of Apriori
- Evaluation of association learning
- POC – To make a model to predict the relationship between frequently bought products together on the given dataset from a supermarket.
-
Module 9 – Natural Language Processing with NLTK
-
Module 10 – Deep Learning with Tensorflow and Keras
-
Module 11 – Rest API with SQL CRUD Operations Flask
- What is SQL?
- Why do we need SQL integration with Python?
- Data types in SQL
- DDL, DML, and TCL sub-languages in SQL
- Significance and type of Joins in SQL
- Where clause in SQL
- Group by clause in SQL
- Create command in SQL
- Insert command in SQL
- Select command in SQL
- Select command variants in SQL
- Update command in SQL
- Delete command in SQL
- Drop command in SQL
- Truncate command in SQL
- Commit and rollback concepts in SQL
- REST principles
- Creating application endpoints
- Implementing endpoints Using Postman for API testing
- Python, database, and front-end integration concepts and implementation
-
Module 12 – Rest API Integration with Databases Web App Development
-
Module 13 – Major Projects
- Computer learning
- Modeling, statistics
- Programming
- Databases
- Business managers
- Information technology managers
- Managers in data science
- etc.