MATLAB Fundamentals, Data Science & Report Generation Training Course

This is a 5 day introduction to Data Science and Artificial Intelligence (AI).

The course is delivered with examples and exercises using Python 

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di tempat) ini ditujukan untuk peserta tingkat menengah yang ingin mengotomatiskan dan mengelola alur kerja pembelajaran mesin, termasuk pelatihan model, validasi, dan penerapan menggunakan Apache Airflow.

Pada akhir pelatihan ini, peserta akan dapat:

• Siapkan Apache Airflow untuk orkestrasi alur kerja pembelajaran mesin.
• Otomatisasi praproses data, pelatihan model, dan tugas validasi.
• Integrasikan Airflow dengan kerangka kerja dan alat pembelajaran mesin.
• Terapkan model pembelajaran mesin menggunakan jalur otomatis.
• Pantau dan optimalkan alur kerja pembelajaran mesin dalam produksi.

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di tempat) ini ditujukan untuk ilmuwan data yang ingin menggunakan ekosistem Anaconda untuk menangkap, mengelola, dan menyebarkan paket dan alur kerja analisis data dalam satu platform.

Pada akhir pelatihan ini, peserta akan dapat:

• Instal dan konfigurasikan Anaconda komponen dan pustaka.
• Memahami konsep inti, fitur, dan manfaat Anaconda.
• Kelola paket, lingkungan, dan saluran menggunakan Anaconda Navigator.
• Gunakan paket Conda, R, dan Python untuk ilmu data dan pembelajaran mesin.
• Ketahui beberapa kasus penggunaan praktis dan teknik untuk mengelola berbagai lingkungan data.

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di tempat) ini ditujukan untuk ilmuwan data dan analis tingkat menengah yang ingin menggunakan AWS Cloud9 untuk alur kerja ilmu data yang efisien.

Pada akhir pelatihan ini, peserta akan dapat:

• Siapkan lingkungan ilmu data di AWS Cloud9.
• Lakukan analisis data menggunakan Python, R, dan Jupyter Notebook di Cloud9.
• Integrasikan AWS Cloud9 dengan layanan data AWS seperti S3, RDS, dan Redshift.
• Memanfaatkan AWS Cloud9 untuk pengembangan dan penerapan model pembelajaran mesin.
• Optimalkan alur kerja berbasis cloud untuk analisis dan pemrosesan data.

Overview

Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month.

Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored.

With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.)

This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain.

Course objectives

Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following:

• Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective
• How Big Data analytic differs from legacy data analytic
• In-house justification of Big Data -Telco perspective
• Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem
• How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach
• Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco
• Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies
• Network failure and service failure analytics from Network meta-data and IPDR
• Financial analysis-fraud, wastage and ROI estimation from sales and operational data
• Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data
• Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space
• Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization

Target Audience

• Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office.
• Business Analysts in Telco
• CFO office managers/analysts
• Operational managers
• QA managers

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di lokasi) ini ditujukan untuk ilmuwan data tingkat pemula dan profesional TI yang ingin mempelajari dasar-dasar ilmu data menggunakan Google Colab.

Pada akhir pelatihan ini, peserta akan mampu:

• Siapkan dan navigasikan Google Colab.
• Tulis dan jalankan kode dasar Python.
• Impor dan tangani kumpulan data.
• Buat visualisasi menggunakan Python perpustakaan.

Participants who complete this training will gain a practical, real-world understanding of Data Science and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Data Science, then progresses into the tools and methodologies used in Data Science.

Audience

• Developers
• Technical analysts
• IT consultants

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

The explosion of information and data in today’s world is un-paralleled, our ability to innovate and push the boundaries of the possible is growing faster than it ever has. The role of Data Scientist is one of the highest in-demand skills across industry today.

We offer much more than learning through theory; we deliver practical, marketable skills that bridge the gap between the world of academia and the demands of industry.

This 7 week curriculum  can be tailored to your specific Industry requirements, please contact us for further information or visit the Nobleprog Institute website 

Audience:

This programme is aimed post level graduates as well as anyone with the required pre-requisite skills which will be determined by an assessment and interview. 

Delivery:

Delivery of the course will be a mixture of Instructor Led Classroom and Instructor Led Online; typically the 1st week will be 'classroom led', weeks 2 - 6 'virtual classroom' and week 7  back to 'classroom led'. 

Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

This course is meant for Marketing Sales Professionals who are intending to get deeper into application of data science in Marketing/ Sales. The course provides
detailed coverage of different data science techniques used for “upsale”, “cross-sale”, market segmentation, branding and CLV.

Difference of Marketing and Sales - How is that sales and marketing are different?

In very simplewords, sales can be termed as a process which focuses or targets on individuals or small groups. Marketing on the other hand targets a larger group or the general public. Marketing includes research (identifying needs of the customer), development of products (producing innovative products) and promoting the product (through advertisements) and create awareness about the product among the consumers. As such marketing means generating leads or prospects. Once the product is out in the market, it is the task of the sales person to persuade the customer to buy the product. Sales means converting the leads or prospects into purchases and orders, while marketing is aimed at longer terms, sales pertain to shorter goals.

This instructor-led, live training (online or onsite) is aimed at professionals who wish to start a career in Data Science.

By the end of this training, participants will be able to:

• Install and configure Python and MySql.
• Understand what Data Science is and how it can add value to virtually any business. 
• Learn the fundamentals of coding in Python
• Learn supervised and unsupervised Machine Learning techniques, and how to implement them and interpret the results.  

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Indonesia (online or onsite) introduces the idea of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea".  It walks participants through the creation of a sample data science project based on top of the Jupyter ecosystem.

By the end of this training, participants will be able to:

• Install and configure Jupyter, including the creation and integration of a team repository on Git.
• Use Jupyter features such as extensions, interactive widgets, multiuser mode and more to enable project collaboraton.
• Create, share and organize Jupyter Notebooks with team members.
• Choose from Scala, Python, R, to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface.

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di tempat) ini ditujukan untuk ilmuwan data dan pengembang yang ingin belajar dan membangun karier mereka di Data Science menggunakan Kaggle.

Pada akhir pelatihan ini, peserta akan dapat:

• Pelajari tentang ilmu data dan pembelajaran mesin.
• Jelajahi analitik data.
• Pelajari tentang Kaggle dan cara kerjanya.

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di tempat) ini ditujukan untuk ilmuwan data dan pengembang yang ingin menggunakan Modin untuk membangun dan menerapkan komputasi paralel dengan Pandas untuk analisis data yang lebih cepat.

Pada akhir pelatihan ini, peserta akan dapat:

• Siapkan lingkungan yang diperlukan untuk mulai mengembangkan Pandas alur kerja berskala dengan Modin.
• Memahami fitur, arsitektur, dan keuntungan Modin.
• Ketahui perbedaan antara Modin, Dask, dan Ray.
• Lakukan operasi Pandas lebih cepat dengan Modin.
• Terapkan seluruh API dan fungsi Pandas.

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di lokasi) ditujukan untuk ilmuwan data dan pengembang yang ingin menggunakan RAPIDS untuk membangun alur data, alur kerja, dan visualisasi yang dipercepat GPU, dengan menerapkan algoritme pembelajaran mesin, seperti XGBoost, cuML, dll.

Pada akhir pelatihan ini, peserta akan mampu:

• Siapkan lingkungan pengembangan yang diperlukan untuk membangun model data dengan NVIDIA RAPIDS.
• Memahami fitur, komponen, dan kelebihan RAPIDS.
• Manfaatkan GPU untuk mempercepat jalur data dan analitik ujung ke ujung.
• Menerapkan persiapan data yang dipercepat GPU dan ETL dengan cuDF dan Apache Arrow.
• Pelajari cara melakukan tugas pembelajaran mesin dengan algoritma XGBoost dan cuML.
• Bangun visualisasi data dan jalankan analisis grafik dengan cuXfilter dan cuGraph.