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Cisco utbildning

Insoft Services är en av få utbildningsleverantörer inom EMEAR som erbjuder hela utbudet av Cisco-certifiering och specialiserad teknikutbildning.

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Cisco-certifieringar

Upplev en blandad inlärningsmetod som kombinerar det bästa av instruktörsledd utbildning och e-lärande i egen takt för att hjälpa dig att förbereda dig för ditt certifieringsprov.

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Cisco Learning Credits

Cisco Learning Credits (CLC) är förbetalda utbildningskuponger som löses in direkt med Cisco och som gör det enklare att planera för din framgång när du köper Ciscos produkter och tjänster.

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Cisco Fortbildning

Ciscos fortbildningsprogram erbjuder alla aktiva certifikatinnehavare flexibla alternativ för att omcertifiera genom att slutföra en mängd olika kvalificerade utbildningsartiklar.

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Cisco Digital Learning

Certifierade medarbetare är VÄRDERADE tillgångar. Utforska Ciscos officiella digitala utbildningsbibliotek för att utbilda dig själv genom inspelade sessioner.

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Partner för affärsaktivering

Cisco Business Enablement Partner Program fokuserar på att vässa affärskunskaperna hos Cisco Channel Partners och kunder.

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Cisco Kurskatalog

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Fortinet-certifieringar

Fortinet Network Security Expert (NSE) -programmet är ett utbildnings- och certifieringsprogram på åtta nivåer för att lära ingenjörer om deras nätverkssäkerhet för Fortinet FW-färdigheter och erfarenheter.

Tekniska utbildningar

Tekniska utbildningar

Insoft är erkänt som Fortinet Authorized Training Center på utvalda platser i EMEA.

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Fortinet Kurskatalog

Utforska ett brett utbud av Fortinet-scheman i olika länder samt onlinekurser.

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ATC-status

Kolla in vår ATC-status i utvalda länder i Europa.

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Fortinet Professionella tjänster

Globalt erkända team av certifierade experter hjälper dig att göra en smidigare övergång med våra fördefinierade konsult-, installations- och migreringspaket för ett brett utbud av Fortinet-produkter.

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Microsoft-utbildning

Insoft Services tillhandahåller Microsoft-utbildning i EMEAR. Vi erbjuder Microsofts tekniska utbildnings- och certifieringskurser som leds av instruktörer i världsklass.

Tekniska utbildningar

Extreme-utbildning

Lär dig exceptionella kunskaper och färdigheter i Extreme Networks.

Technische Kurse

Tekniske-certifieringar

Vi tillhandahåller omfattande läroplan för tekniska kompetensfärdigheter på certifieringsprestationen.

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Extreme Kurskatalog

Hier finden Sie alle Extreme Networks online und den von Lehrern geleiteten Kalender für den Klassenraum.

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ATP-ackreditering

Som auktoriserad utbildningspartner (ATP) säkerställer Insoft Services att du får de högsta tillgängliga utbildningsstandarderna.

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Konsultpaket

Vi erbjuder innovativt och avancerat stöd för att designa, implementera och optimera IT-lösningar.Vår kundbas inkluderar några av de största telekombolagen globalt.

Lösningar och tjänster

Globalt erkända team av certifierade experter hjälper dig att göra en smidigare övergång med våra fördefinierade konsult-, installations- och migreringspaket för ett brett utbud av Fortinet-produkter.

Om oss

Insoft Tillhandahåller auktoriserade utbildnings- och konsulttjänster för utvalda IP-leverantörer.Lär dig hur vi revolutionerar branschen.

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    • Data Science for Marketing Analytics

    Enroll Now
    Duration
    3 Dagar
    Delivery
    (Online och på plats)
    Price
    Pris på begäran
    The Data Science for Marketing Analytics course, covers every stage of data analytics, from working with a raw dataset to segmenting a population and modelling different parts of the population based on the segments. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modelling customer product choices. By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.  

    Lesson One: Data Preparation and Cleaning

    • Data Models and Structured Data
    • pandas
    • Data Manipulation

    Lesson Two: Data Exploration and Visualization

    • Identifying the Right Attributes
    • Generating Targeted Insights
    • Visualizing Data

    Lesson Three: Unsupervised Learning: Customer Segmentation

    • Customer Segmentation Methods
    • Similarity and Data Standardization
    • k-means Clustering

    Lesson Four: Choosing the Best Segmentation Approach

    • Choosing the Number of Clusters
    • Different Methods of Clustering
    • Evaluating Clustering

    Lesson Five: Predicting Customer Revenue Using Linear Regression

    • Understanding Regression
    • Feature Engineering for Regression
    • Performing and Interpreting Linear Regression

    Lesson Six: Other Regression Techniques and Tools for Evaluation

    • Evaluating the Accuracy of a Regression Model
    • Using Regularization for Feature Selection
    • Tree-Based Regression Models

    Lesson Seven: Supervised Learning: Predicting Customer Churn

    • Classification Problems
    • Understanding Logistic Regression
    • Creating a Data Science Pipeline

    Lesson Eight: Fine-Tuning Classification Algorithms

    • Support Vector Machine
    • Decision Trees
    • Random Forest
    • Preprocessing Data for Machine Learning Models
    • Model Evaluation
    • Performance Metrics

    Lesson Nine: Modeling Customer Choice

    • Understanding Multiclass Classification
    • Class Imbalanced Data

    Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts.

    It’ll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.

     

    Hardware:

    For an optimal student experience, we recommend the following hardware configuration:

    • Processor: Dual Core or better
    • Memory: 4 GB RAM
    • Storage: 10 GB available space

     

    Software:

    You’ll also need the following software installed in advance:

    • Any of the following operating systems: Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit, or Windows 10 32/64-bit, Ubuntu 14.04 or later, or macOS Sierra or later.
    • Browser: Google Chrome or Mozilla Firefox
    • Conda
    • Python 3.x
    Overview
    The Data Science for Marketing Analytics course, covers every stage of data analytics, from working with a raw dataset to segmenting a population and modelling different parts of the population based on the segments. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modelling customer product choices. By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.  

    Lesson One: Data Preparation and Cleaning

    • Data Models and Structured Data
    • pandas
    • Data Manipulation

    Lesson Two: Data Exploration and Visualization

    • Identifying the Right Attributes
    • Generating Targeted Insights
    • Visualizing Data

    Lesson Three: Unsupervised Learning: Customer Segmentation

    • Customer Segmentation Methods
    • Similarity and Data Standardization
    • k-means Clustering

    Lesson Four: Choosing the Best Segmentation Approach

    • Choosing the Number of Clusters
    • Different Methods of Clustering
    • Evaluating Clustering

    Lesson Five: Predicting Customer Revenue Using Linear Regression

    • Understanding Regression
    • Feature Engineering for Regression
    • Performing and Interpreting Linear Regression

    Lesson Six: Other Regression Techniques and Tools for Evaluation

    • Evaluating the Accuracy of a Regression Model
    • Using Regularization for Feature Selection
    • Tree-Based Regression Models

    Lesson Seven: Supervised Learning: Predicting Customer Churn

    • Classification Problems
    • Understanding Logistic Regression
    • Creating a Data Science Pipeline

    Lesson Eight: Fine-Tuning Classification Algorithms

    • Support Vector Machine
    • Decision Trees
    • Random Forest
    • Preprocessing Data for Machine Learning Models
    • Model Evaluation
    • Performance Metrics

    Lesson Nine: Modeling Customer Choice

    • Understanding Multiclass Classification
    • Class Imbalanced Data

    Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts.

    It’ll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.

     

    Hardware:

    For an optimal student experience, we recommend the following hardware configuration:

    • Processor: Dual Core or better
    • Memory: 4 GB RAM
    • Storage: 10 GB available space

     

    Software:

    You’ll also need the following software installed in advance:

    • Any of the following operating systems: Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit, or Windows 10 32/64-bit, Ubuntu 14.04 or later, or macOS Sierra or later.
    • Browser: Google Chrome or Mozilla Firefox
    • Conda
    • Python 3.x
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      Datum på begäran

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