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Data Science, Statistics with Python This course Start with introduction to Data Science and Statistics using Python. It covers both the aspects of Statistical concepts and the practical implementation using Python. If you’re new to Python, don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems. Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Data frames to manipulate data with ease. Machine Learning and Data Science : Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets. Real life examples: Every concept is explained with the help of examples, case studies and source code wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant. finance context.
1. Comprehensive Skill Development : These courses provide a comprehensive understanding of the entire data science pipeline, from data collection and preprocessing to modeling,evaluation, and deployment. You’ll gain proficiency in a wide range of tools, techniques, and algorithms used in data analysis and machine learning.
2. In-Demand Skills : Data science and machine learning are among the most sought-after skills in today’s job market. By completing a course in this field, you’ll equip yourself with highly valuable skills that are in high demand across various industries, leading to diverse career opportunities and competitive salaries.
3. Real-World Projects : include hands-on projects and case studies that allow you to apply your skills to real-world problems and datasets. These projects help you build a portfolio of work that demonstrates your practical expertise to potential employers.
4. Career Advancement : Data science and machine learning skills are essential for advancing in many fields, including data analysis, artificial intelligence, business intelligence, and more. By completing this course in this area, you’ll enhance your qualifications and increase your chances of career advancement and promotion.
5. Flexibility and Accessibility : Radical Technologies Data science and machine learning are available online, making them accessible to learners from anywhere in the world. Online courses offer flexibility in terms of scheduling and pacing, allowing you to learn at your own pace while balancing other commitments.
6. Networking Opportunities : Participating in a course in data science and machine learning with Radical Technologies gives you the opportunity to connect with instructors, industry professionals, and fellow learners who share your interests. These connections can provide valuable networking opportunities and may lead to collaborations, mentorship, or job referrals in the future.
7. Continuous Learning : The field of data science and machine learning is constantly evolving, with new techniques, algorithms, and tools emerging regularly. By completing a course with Radical Technologies , you’ll develop a strong foundation of knowledge and skills that you can continue to build upon through self-study, professional development, and advanced courses.
8. Problem-Solving Skills : Data science and machine learning involve solving complex problems using data-driven approaches. By completing a course in this field, you’ll develop critical thinking, problem-solving, and analytical skills that are valuable in many domains and industries.
9. Industry-Relevant Curriculum : Radical Technologies designed this course in collaboration with industry experts and incorporate the latest trends, technologies, and best practices used in the field of data science and machine learning. This ensures that you’re learning relevant skills that are directly applicable to real-world challenges and scenarios.
1. Comprehensive Skill Development : These courses provide a comprehensive understanding of the entire data science pipeline, from data collection and preprocessing to modeling,evaluation, and deployment. You’ll gain proficiency in a wide range of tools, techniques, and algorithms used in data analysis and machine learning.
2. In-Demand Skills : Data science and machine learning are among the most sought-after skills in today’s job market. By completing a course in this field, you’ll equip yourself with highly valuable skills that are in high demand across various industries, leading to diverse career opportunities and competitive salaries.
3. Real-World Projects : include hands-on projects and case studies that allow you to apply your skills to real-world problems and datasets. These projects help you build a portfolio of work that demonstrates your practical expertise to potential employers.
4. Career Advancement : Data science and machine learning skills are essential for advancing in many fields, including data analysis, artificial intelligence, business intelligence, and more. By completing this course in this area, you’ll enhance your qualifications and increase your chances of career advancement and promotion.
5. Flexibility and Accessibility : Radical Technologies Data science and machine learning are available online, making them accessible to learners from anywhere in the world. Online courses offer flexibility in terms of scheduling and pacing, allowing you to learn at your own pace while balancing other commitments.
6. Networking Opportunities : Participating in a course in data science and machine learning with Radical Technologies gives you the opportunity to connect with instructors, industry professionals, and fellow learners who share your interests. These connections can provide valuable networking opportunities and may lead to collaborations, mentorship, or job referrals in the future.
7. Continuous Learning : The field of data science and machine learning is constantly evolving, with new techniques, algorithms, and tools emerging regularly. By completing a course with Radical Technologies , you’ll develop a strong foundation of knowledge and skills that you can continue to build upon through self-study, professional development, and advanced courses.
8. Problem-Solving Skills : Data science and machine learning involve solving complex problems using data-driven approaches. By completing a course in this field, you’ll develop critical thinking, problem-solving, and analytical skills that are valuable in many domains and industries.
9. Industry-Relevant Curriculum : Radical Technologies designed this course in collaboration with industry experts and incorporate the latest trends, technologies, and best practices used in the field of data science and machine learning. This ensures that you’re learning relevant skills that are directly applicable to real-world challenges and scenarios.
Course content summary -Introduction to Data Science with Python
Python Essentials
NumPy, pandas, scikit-learn, stat models, nltk
Accessing/Importing And Exporting Data Using Python Modules
Data Manipulation – Cleansing – Munging using python modules
Data Analysis – Visualization Using Python
Introduction to Statistics
Introduction to Predictive Modelling
Data Exploration For Modelling
Data Preparation
Segmentation: Solving Segmentation Problems
Linear Regression: Solving Regression Problems
Logistic Regression : Solving Classification Problems
Time Series Forecasting : Solving Forecasting Problems
Machine Learning
Unsupervised Learning : Segmentation
Supervised Learning :- Decision Trees
Supervised Learning :- Ensemble Learning
Supervised Learning :- Artificial Neural Network – ANN
Supervised Learning :- Support Vector Machines
Supervised Learning :-KNN
Supervised Learning :- Naive Bayes
Text Mining And Analytics
You should be able to demonstrate these skills
This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.
Course Contains
Introduction to Data Science with Python
Outline for this course
Introduction to Data Science with Python
Python Essentials
Accessing/Importing And Exporting Data Using Python Modules
Data Manipulation – Cleansing – Munging using python modules
Data Analysis – Visualization Using Python
Introduction to Statistics
Introduction to Predictive Modelling
Data Exploration For Modelling
Data Preparation
Segmentation: Solving Segmentation Problems
Linear Regression: Solving Regression Problems
Logistic Regression : Solving Classification Problems
Unsupervised Learning : Segmentation
Supervised Learning :- Decision Trees
Time Series Forecasting : Solving Forecasting Problems
Machine Learning : Predictive Modelling
Supervised Learning :- Ensemble Learning
Supervised Learning :- Artificial Neural Network – ANN
Supervised Learning :- Support Vector Machines
Supervised Learning :-KNN
Supervised Learning :- Naive Bayes
Text Mining And Analytics
Audience for this course
+91 8882400500
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1. Basic user account management (creating, modifying, and deleting users).
2. Password resets and account unlocks.
3. Basic file system navigation and management (creating, deleting, and modifying files and directories).
4. Basic troubleshooting of network connectivity issues.
5. Basic software installation and package management (installing and updating software packages).
6. Viewing system logs and checking for errors or warnings.
7. Running basic system health checks (CPU, memory, disk space).
8. Restarting services or daemons.
9. Monitoring system performance using basic tools (top, df, free).
10. Running basic commands to gather system information (uname, hostname, ifconfig).
1. Intermediate user account management (setting permissions, managing groups).
2. Configuring network interfaces and troubleshooting network connectivity issues.
3. Managing file system permissions and access control lists (ACLs).
4. Performing backups and restores of files and directories.
5. Installing and configuring system monitoring tools (Nagios, Zabbix).
6. Analyzing system logs for troubleshooting purposes.
7. Configuring and managing software repositories.
8. Configuring and managing system services (systemd, init.d).
9. Performing system updates and patch management.
10. Monitoring and managing system resources (CPU, memory, disk I/O).
1. Advanced user account management (LDAP integration, single sign-on).
2. Configuring and managing network services (DNS, DHCP, LDAP).
3. Configuring and managing storage solutions (RAID, LVM, NFS).
4. Implementing and managing security policies (firewall rules, SELinux).
5. Implementing and managing system backups and disaster recovery plans.
6. Configuring and managing virtualization platforms (KVM, VMware).
7. Performance tuning and optimization of system resources.
8. Implementing and managing high availability solutions (clustering, load balancing).
9. Automating system administration tasks using scripting (Bash, Python).
10. Managing system configurations using configuration management tools (Ansible, Puppet).
1. Learning basic shell scripting for automation tasks. 2. Understanding file system permissions and ownership. 3. Learning basic networking concepts (IP addressing, routing). 4. Learning how to use package management tools effectively. 5. Familiarizing with common Linux commands and utilities. 6. Understanding basic system architecture and components. 7. Learning basic troubleshooting techniques and methodologies. 8. Familiarizing with basic security principles and best practices. 9. Learning how to interpret system logs and diagnostic output. 10. Understanding the role and importance of system backups and restores.
1. Advanced scripting and automation techniques (error handling, loops).
2. Understanding advanced networking concepts (VLANs, subnetting).
3. Familiarizing with advanced storage technologies (SAN, NAS).
4. Learning advanced security concepts and techniques (encryption, PKI).
5. Understanding advanced system performance tuning techniques.
6. Learning advanced troubleshooting methodologies (root cause analysis).
7. Implementing and managing virtualization and cloud technologies.
8. Configuring and managing advanced network services (VPN, IDS/IPS).
9. Implementing and managing containerization technologies (Docker, Kubernetes).
10. Understanding enterprise-level IT governance and compliance requirements.
1. Designing and implementing complex IT infrastructure solutions. 2. Architecting and implementing highly available and scalable systems. 3. Developing and implementing disaster recovery and business continuity plans. 4. Conducting security audits and vulnerability assessments. 5. Implementing and managing advanced monitoring and alerting systems. 6. Developing custom automation solutions tailored to specific business needs. 7. Providing leadership and mentorship to junior team members. 8. Collaborating with other IT teams on cross-functional projects. 9. Evaluating new technologies and making recommendations for adoption. 10. Participating in industry conferences, workshops, and training programs.
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(Our Team will call you to discuss the Fees)
(Our Team will call you to discuss the Fees)