Predictive Analytics Training in Indonesia

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di tempat) ini ditujukan untuk pengembang tingkat menengah yang ingin membangun aplikasi bertenaga AI menggunakan analisis prediktif dan model generatif. Pada akhir pelatihan ini, peserta akan dapat:

• Memahami dasar-dasar AI prediktif dan model generatif.
• Memanfaatkan alat bertenaga AI untuk pengkodean prediktif, peramalan, dan otomatisasi.
• Terapkan LLM (Model Bahasa Besar) dan transformer untuk pembuatan teks dan kode.
• Terapkan peramalan deret waktu dan rekomendasi berbasis AI.
• Mengembangkan dan menyempurnakan model AI untuk aplikasi dunia nyata.
• Mengevaluasi pertimbangan etika dan praktik terbaik dalam penerapan AI.

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di lokasi) ini ditujukan untuk profesional TI tingkat pemula yang ingin memahami dasar-dasar Predictive AI. Pada akhir pelatihan ini, peserta akan mampu:

• Memahami konsep inti AI Prediktif dan aplikasinya.
• Kumpulkan, bersihkan, dan proses awal data untuk analisis prediktif.
• Jelajahi dan visualisasikan data untuk mengungkap wawasan.
• Membangun model statistik dasar untuk membuat prediksi.
• Evaluasi kinerja model prediktif.
• Terapkan konsep AI Prediktif ke skenario dunia nyata.

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di lokasi) ditujukan untuk profesional tingkat menengah DevOps yang ingin mengintegrasikan AI prediktif ke dalam praktik DevOps mereka. Pada akhir pelatihan ini, peserta akan mampu:

• Menerapkan model analitik prediktif untuk memperkirakan dan memecahkan tantangan di jalur DevOps.
• Memanfaatkan alat berbasis AI untuk meningkatkan pemantauan dan operasi.
• Terapkan teknik pembelajaran mesin untuk meningkatkan alur kerja pengiriman perangkat lunak.
• Rancang strategi AI untuk penyelesaian dan optimalisasi masalah secara proaktif.
• Arahkan pertimbangan etis dalam penggunaan AI di DevOps.

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

Dalam pelatihan langsung yang dipandu instruktur di Indonesia ini, peserta akan mempelajari pola pikir untuk mendekati Big Data teknologi, menilai dampaknya terhadap proses dan kebijakan yang ada, dan menerapkan teknologi ini untuk tujuan mengidentifikasi aktivitas kriminal dan mencegah kejahatan. Studi kasus dari organisasi penegak hukum di seluruh dunia akan diperiksa untuk mendapatkan wawasan tentang pendekatan adopsi, tantangan, dan hasil mereka. Pada akhir pelatihan ini, peserta akan dapat:

• Gabungkan teknologi Big Data dengan proses pengumpulan data tradisional untuk menyusun cerita selama investigasi.
• Terapkan solusi penyimpanan dan pemrosesan data besar industri untuk analisis data.
• Siapkan proposal untuk penerapan alat dan proses yang paling memadai untuk memungkinkan pendekatan berbasis data terhadap investigasi kriminal.

Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run, so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.

Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di tempat) ini ditujukan untuk ilmuwan data dan analis data yang ingin mengotomatiskan, mengevaluasi, dan mengelola model prediktif menggunakan kemampuan pembelajaran mesin DataRobot. Pada akhir pelatihan ini, peserta akan dapat:

• Muat kumpulan data dalam DataRobot untuk menganalisis, menilai, dan memeriksa kualitas data.
• Bangun dan latih model untuk mengidentifikasi variabel penting dan memenuhi target prediksi.
• Menafsirkan model untuk menciptakan wawasan berharga yang berguna dalam pengambilan keputusan bisnis.
• Pantau dan kelola model untuk mempertahankan kinerja prediksi yang optimal.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events. In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data. By the end of this training, participants will be able to:

• Create predictive models to analyze patterns in historical and transactional data
• Use predictive modeling to identify risks and opportunities
• Build mathematical models that capture important trends
• Use data from devices and business systems to reduce waste, save time, or cut costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

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

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

RapidMiner adalah platform perangkat lunak ilmu data sumber terbuka untuk pembuatan prototipe dan pengembangan aplikasi yang cepat. Platform ini mencakup lingkungan terintegrasi untuk persiapan data, pembelajaran mesin, pembelajaran mendalam, penambangan teks, dan analisis prediktif. Dalam pelatihan langsung yang dipandu instruktur ini, peserta akan mempelajari cara menggunakan RapidMiner Studio untuk persiapan data, pembelajaran mesin, dan penerapan model prediktif. Pada akhir pelatihan ini, peserta akan dapat:

• Instal dan konfigurasikan RapidMiner
• Siapkan dan visualisasikan data dengan RapidMiner
• Validasi model pembelajaran mesin
• Gabungkan data dan buat model prediktif
• Mengoperasionalkan analitik prediktif dalam proses bisnis
• Pecahkan masalah dan optimalkan RapidMiner

Hadirin

• Ilmuwan data
• Insinyur
• Pengembang

Format Kursus

• Sebagian kuliah, sebagian diskusi, latihan dan praktik langsung yang berat

Catatan

• Untuk meminta pelatihan khusus untuk kursus ini, silakan hubungi kami untuk mengaturnya.