The 5 best marketing analytics with python 2019
Finding the best marketing analytics with python suitable for your needs isnt easy. With hundreds of choices can distract you. Knowing whats bad and whats good can be something of a minefield. In this article, weve done the hard work for you.
Best marketing analytics with python
1. Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)
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Pearson FT PressDescription
Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications.
Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis.
Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes:
- The role of analytics in delivering effective messages on the web
- Understanding the web by understanding its hidden structures
- Being recognized on the web and watching your own competitors
- Visualizing networks and understanding communities within them
- Measuring sentiment and making recommendations
- Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics
Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R.
Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.
2. Data Analysis For Social Science & Marketing Research using Python: A Non-Programmer's Guide
Description
The book is written for researchers in social science and marketing field, especially for those with little or no knowledge in computer programming. Data analytics has become part and parcel in the contemporary technologically fast paced world. We have amazing tools and software that allow us to analyse data available in various formats. However, most of the popular paid software and packages for data analysis is not affordable or not even accessible for the students, researchers. This is true in the case of many NGOs and agencies how are involved in community based research in developing countries. We have popular open source platforms and tools such as R and Python for data analysis. This book makes use of Python because of its simplicity, adaptability, broader scope and greater potential in advanced data mining and text mining contexts. We found it as a need to educate and train the researchers from social science and marketing research background, so that they could make use of Python, a promising tool to meet simple to extremely complex data analyses needs free of cost. The learnings from this book will not only help them in doing their conventional data analyses but also enable them to pursue advanced knowledge in machine learning algorithms, text analytics and other new generation techniques with the support of freely accessible open source platforms. Since the objective of the book is to educate the researchers with no programming background, we have made every effort to give hands-on experience in learning some basic coding in Python, which is sufficient for the readers to follow the book. The step-by-step procedure to do various data processing and analysis described in this book will make it easy for the users. Apart from that, we have tried our level best to give explanations on specific codes and how they perform to get us the desired output. We also request you to give you valuable comments and suggestions on the book, via our blog, so that we could improve the same in the upcoming volumes. We commit ourselves to providing explanations to the readers questions related to the codes and analysis provided in this book. The book specifically deals with data sets of row and column format, as the general format commonly used in social science research, which most of the researchers are familiar with. So we do not work with arrays and dictionaries, except in one or two occasions (only to make you familiar with that) instead prefer to make use of Excel data and pandas data frame. The book consists of thirteen chapters. The first chapter gives an introduction to Python and its relevance and scope in contemporary data analysis contexts. Ch. 2 teaches the basics and Python coding, Ch. 3-7, provide a step-by-step narration of how to enter data, process it, preliminary analysis and data cleaning with the help of Python, Ch.8-9, present data visualizations and narration techniques using Python; Ch.10.demonstrate how Python can use for statistical analysis. The remaining chapters are focusing on giving more real life situations in data analysis and the practical solutions to handle them. The exercises provided in the book are similar to real analysis situations, and that will help the reader for an easy transition to the data analyst jobs. The authors have taken utmost care identifying and providing solutions to all practical difficulties the readers may face while using Python for data analysis purpose. The authors have developed a series of codes and have incorporated them to make data processing and analysis convenient and easy for the researchers. The self-learning materials given in this book will help social science and marketing researchers to deepen their understanding of various steps in data processing and analyses and to gain advanced skills in using Python for this purpose.3. Marketing Data Science: Modeling Techniques In Predictive Analytics With R And Python
Description
Please Read Notes: Brand New, International Softcover Edition, Printed in black and white pages, minor self wear on the cover or pages, Sale restriction may be printed on the book, but Book name, contents, and author are exactly same as Hardcover Edition. Fast delivery through DHL/FedEx express.4. Statistics: Practical Concept of Statistics for Data Scientists
Description
Are you trying to get started with learning statistics?
This book is your answer.
Statistics are not a tool but rather a set of techniques that you have access to that will help you analyze a set of data that you either generate, receive, or give. Statistics are absolutely vital for those attempting to study Big Data because it allows the scientists studying the data to make sense of the information when the information is on such a large and global scale. Unlike local neighborhood statistics or marketing statistics, big data encompasses a huge range of information and often this big data will be populated by thousands if not millions of data points. Statistics help you break down these data points so that you can reasonably understand them and work with the data that comes into you.
Here's What's Included In This Book
- Basics of Statistics
- Exploratory Data Analysis
- Different Sampling Methods
- Different Types of Structured Data
- Run Charts and Statistical Process Control
- Variation Analysis
- Practical Application of Statistics
5. Data Science Fundamentals for Marketing and Business Professionals - Training DVD
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Learn Data Science Fundamentals for Marketing and Business Professionals from a professional trainer from your own desk.Visual training method, offering users increased retention and accelerated learning
Breaks even the most complex applications down into simplistic steps.
Easy to follow step-by-step lessons, ideal for all
Description
Number of Videos: 1 hour - 18 lessonsShips on: DVD-ROM
User Level: Beginner
What do data scientists and analysts do? What software languages do they use and what soft and hard skills are required? Data science evangelist Tomi Mester answers these questions and more in this peek into the work world of data professionals. You'll get an introduction into how to use coding, statistics and business thinking for data projects. You'll see a demonstration of data science's three essential languages (SQL, Python, and R). As you explore the types of business thinking that data professionals use, Tomi will show you the statistical tools and methods data scientists and analysts use in their jobs, and you'll learn about the pathways you can take to become a data scientist.
- Understand what data scientists and analysts do, how they work, and how they think
- Learn about the three data languages every data scientist and analyst must know
- Improve your ability to effectively communicate with data professionals
- Pick up tips on how you can get hands-on data science experience
- Discover how you can become a data analyst or data scientist
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