DATA SCIENCE FOR STARTUPS: AN INTRODUCTION | xccelerate

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Data Science for Startups: An Introduction xccelerate.co/blog/data-science-for-startups This article explores the use of data science for startups that essentially covers the importance and impact of the data pipeline data extraction and tracking predictive modeling and business intelligence. We are going to absorb the brief idea of building data platforms and functional features to utilize the best power of data including the entire data discipline. 1 / 8

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Since in recent years the data science domain has evolved in its scope opportunities and promises it is important for data scientists to realize the effective role and value of dynamic data analysis scalable models deep learning data processors and running experiments. You will see what factors and features to consider while building an impactful data science platform and products with a solid data pipeline for a start-up company and how to approach the entire idea. Data Science: Importance and Impact The goal of data science products should be to improve and scale the product for startups by means of data-enabled architecture and well-structured data discipline. Currently data science products are designed with the predictive capability to answer questions related to business growth prospects methods to run the business effectively customer behavior and tendencies etc. The importance and big impact of data science on business still varies depending on the goal of organizations and are usually future-focused. Here are a few great benefits of using data science for startups: Data extracting and analysis Identifying key business metrics Building Data pipelines Predictive models for customer behavior Business intelligence for highlighting KPIs Experimental models to test product features Visualization of data discoveries Testing and validating product changes Read Also: Data Analytics VS Data Science 2 / 8

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Data Extraction and Tracking Data collection and tracking is a vital part of building a data science model and precedes everything in the process. To analyse all about user behavior your first step should be extracting data about the user base their interactions and the connection with the brand. Startups often get baffled about product progress and customer acquisition due to data deficiency. For instance if your specialization is an e-commerce mobile app it is important to vigilantly keep watch on user engagement timeframe event logs the volume of active sessions number of app installations region-specific attributes spending or the amount of interest in special customer-focused services. Collecting all of this data of actual active app users will lead you in realizing where you stand and what you should do to reach the maximum business potential. You will gauge the number of users likely and most likely to interact with or possibly buy your product and how. This also includes monitoring the dropout rate users quitting the app customer feedback and effective product improvement ways. To make all of these data-driven operations happen you must embed a target-specific tracking mechanism that essentially involves identifying major events attributes and product features that drive maximum customer attention. Embedded event trackers enable you to collect dynamic data that can be further analysed for better product development. Structure Data Pipelines 3 / 8

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Post data collection it is time to analyse process and deliver the results in real time to the users. A data pipeline is responsible for processing the collected data — which is a crucial part of data science. The data pipeline is basically connected to a strong database platform such as Hadoop or SQL where intense data processing happens. Normally there are 3 types of data startups have to deal with when creating data pipelines: Raw: Usually no schema is applied to raw data and also they are not present in any designated format. Generally the tracking events sent are in the form of raw data and suitable schemas are applied to them in the later stages of the pipeline. Processed: When a schema is applied to raw data it is regarded as processed. Processed data is encoded in specified formats and is stored in a different location in the data pipeline. Cooked: A user event contains multiple attributes based on the usage of the data product. This can be considered as an input to cooked data that can be used to summarize the daily usage of the product. Ideal data pipelines are the ones that can: Offer real-time delivery and access Scalable pipelines that can handle progressively changing data size. Enable data stability safety as changes and updates are introduced. Generate alerts if any notification or data reception errors are sensed. When it comes to startups one must test the components of data pipeline in order to assess its performance data handling speed scalability as well as precision. Read Also: Top 10 Python libraries for Data Science 2020 4 / 8

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Business Intelligence For data scientists working in a startup it is of great importance to transform raw data with no format into cooked data with a user-friendly format that eventually summarizes the future growth and impact of your product. The identification of key metrics of the data product known as KPIs helps you analyze its performance. KPIs are generally used for measurements of startup performance or its data-oriented products. These KPIs tend to capture the details about product engagement growth and retention with respect to the changes implemented within the product. Use of R in data-centric reports Like Python R is another one of the most compelling programing languages used in data science for creating web applications and graphical plots. In addition data scientists can also fully leverage R to build and train models especially focused on generating business performance reports. R-powered data solutions look after manual reporting and turn them into reproducible reporting. This means R eventually helps minimize the cost and effort spent on manual reporting and enable an automated form of report generation. Data Transformation with ETL Extract Transform and Load 5 / 8

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The main duty of ETL is to transform raw data into processed data and processed data into cooked data. ETL processors are configured to transform raw data into cooked data where cooked data is present in the form of aggregated data. Exploratory Data Analysis EDA When the job of setting up a data pipeline is finally done you are down to explore the data in-depth to gain useful insights about product improvement. Thus EDA helps you understand the value type and nature of the data collected determine the relationships between various parameters and attributes and reach valuable insights. Key methods of exploratory data analysis of the data product are: Data plotting Summary statistics Identification of core features Correlation of presented values Development of Predictive Models with ML It is nearly impossible to conceive projects of data science without the power of Machine Learning ML especially when the models are trained to make data-driven predictions. Predictive data architecture helps forecast user behavior. Data science startups can use predictive ML models to design and tune their products to user expectations. Models of this caliber are best implemented for real-time applications where a most accurate recommendation engine is required. For instance you can think of building one for streaming movie apps e-commerce or online app stores. Data science product development 6 / 8

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Data Scientists working for startups can drive growth by contributing to product improvements. However this is a grueling job that demands a smart move from model training to model deployment. While there are tools that help you build strong data products it is not enough to report model specifications as it does not always target the real issue. This is why manifesting information in plots and graphs helps the data science startup team tackle various underlying issues in the model. For smooth deployment and management of scalable data models Google DataFlow is a considerable tool for startups. Experimentation for gradual product improvement While experimenting with new changes to the products the main focus is whether or not the outcome of the new implementation benefits startups and is best received by the customers. For this it is wise to opt for the most commonly preferred A/B Testing. This testing draws statistical conclusions while applying hypothesis testing to compare the two versions of the variables. Read Also: Why Should you use Python for Data Science Summary Regardless of what methods or programming languages you use the ultimate goal of data science for startups should be to enhance the product and make it work better. For any startup it is critical to meet exponential growth and sustain market changes by implementing the best data discipline without any data loss. 7 / 8

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To get their best chance startups must feel compelled to surpass basic data models and adapt to dynamic data pipeline data processors predictive data models and ETLs and experimentation products. Since constant product health improvement is connected to startup growth and decisions data scientists need to train models with the ability to forecast user behavior and responses to products. 8 / 8

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