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These time series forecasting courses are the best to study to follow a career in the time series field.To have the ability to look into the future. Wouldn’t that be fantastic? We’ll undoubtedly get there someday, but time series forecasting can help you get there now. It enables you to “look” ahead of time and achieve success in your business. Time series forecasting is a The foundational knowledge needed to create and apply time series forecasting models in a range of business scenarios is provided in the Time Series Forecasting course. You’ll study the fundamentals of time series data and forecasting models, as well as a lot more. You’ll also learn how to use Alteryx, a data analytics program, to apply what you’ve learned in this course.
This specialization will teach you how to use TensorFlow, a prominent open-source machine learning framework. In this fourth course, you’ll learn how to use TensorFlow to create time series models. To prepare time series data, you’ll first use best practices. You’ll also learn how to use RNNs and 1D ConvNets for prediction. Finally, you’ll put everything you’ve learned thus far into practice by creating a sunspot prediction model based on real-world data.
This course will examine data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, agricultural commodity pricing, and so on. You’ll also look at a number of mathematical models that may be used to describe the processes that produce this type of data, as well as graphical representations that can help you understand your data. Finally, you’ll discover how to construct forecasts that accurately predict what you can expect in the future.
This course covers additional Machine Learning techniques that supplement core tasks, such as forecasting and evaluating censored data. You’ll discover how to locate and analyze data having a time component, as well as censored data that requires outcome inference. You’ll learn a few Time Series Analysis and Survival Analysis approach. This course’s hands-on component focuses on recommended practices and testing assumptions generated from statistical learning.
You will learn how to preprocess time series data, visualize time series data, and compare the time series predictions of four machine learning models in this 2-hour project-based course. You will use the Python programming language to develop time series analysis models to forecast daily deaths caused by SARS-CoV-19, or COVID-19. The following models will be created and trained: SARIMAX, Prophet, neural networks, and XGBOOST. You’ll use the matplotlib library to visualize data, extract features from a time series data set, and partition and normalize the data.
By the completion of this project, you will have a solid understanding of the principles of time-series forecasting, which are used to anticipate web traffic flow in order to give useful business intelligence for operations, resource allocation, and opportunity identification. In Google Sheets, you’ll be able to forecast web traffic as well. To accomplish this, you’ll use the free Google Sheets software to explore trend forecasting and its applications.
You will learn the fundamentals of time series analysis in R in this 2-hour project-based course. You will have created each of the major model types (Autoregressive, Moving Average, ARMA, ARIMA, and decomposition) using a real-world data set to anticipate the future by the end of this project.
This project focuses on time series data analysis in Python for beginners. Only after conducting thorough exploratory research and gaining insight into the data set is model construction effective. The following are the goals: 1. Importing needed libraries and time-series data sets. 2. Review the summary of time-series data and obtain basic descriptive statistics. 3. Make inferences from time-series data visualization graphs 4. Examine how time series data behaves. 5. Convert non-stationary data to stationary data using transformation functions.
On the basis of historical data, predictive models seek to forecast future value. You will analyze the global transmission of the Covid-19 virus and train a time-series model (fbprophet) to predict corona virus-related infections in the United States in this hands-on project.
To have the ability to look into the future. Wouldn’t that be fantastic? We’ll undoubtedly get there someday, but time series forecasting can help you get there now. It enables you to “look” ahead of time and achieve success in your business. Time series forecasting is a machine learning technique that examines data and time sequences to forecast future events. Based on historical time-series data, this methodology delivers near-accurate predictions about future patterns. Today we have listed the top 10-time series forecasting courses to watch out for in 2023. If you are aspiring to carve a career path out of it, you may want to consider these courses The foundational knowledge needed to create and apply time series forecasting models in a range of business scenarios is provided in the Time Series Forecasting course. You’ll study the fundamentals of time series data and forecasting models, as well as a lot more. You’ll also learn how to use Alteryx, a data analytics program, to apply what you’ve learned in this chúng tôi specialization will teach you how to use TensorFlow, a prominent open-source machine learning framework. In this fourth course, you’ll learn how to use TensorFlow to create time series models. To prepare time series data, you’ll first use best practices. You’ll also learn how to use RNNs and 1D ConvNets for prediction. Finally, you’ll put everything you’ve learned thus far into practice by creating a sunspot prediction model based on real-world chúng tôi course will examine data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, agricultural commodity pricing, and so on. You’ll also look at a number of mathematical models that may be used to describe the processes that produce this type of data, as well as graphical representations that can help you understand your data. Finally, you’ll discover how to construct forecasts that accurately predict what you can expect in the chúng tôi course covers additional Machine Learning techniques that supplement core tasks, such as forecasting and evaluating censored data. You’ll discover how to locate and analyze data having a time component, as well as censored data that requires outcome inference. You’ll learn a few Time Series Analysis and Survival Analysis approach. This course’s hands-on component focuses on recommended practices and testing assumptions generated from statistical chúng tôi will learn how to preprocess time series data, visualize time series data, and compare the time series predictions of four machine learning models in this 2-hour project-based course. You will use the Python programming language to develop time series analysis models to forecast daily deaths caused by SARS-CoV-19, or COVID-19. The following models will be created and trained: SARIMAX, Prophet, neural networks, and XGBOOST. You’ll use the matplotlib library to visualize data, extract features from a time series data set, and partition and normalize the chúng tôi the completion of this project, you will have a solid understanding of the principles of time-series forecasting, which are used to anticipate web traffic flow in order to give useful business intelligence for operations, resource allocation, and opportunity identification. In Google Sheets, you’ll be able to forecast web traffic as well. To accomplish this, you’ll use the free Google Sheets software to explore trend forecasting and its chúng tôi will learn the fundamentals of time series analysis in R in this 2-hour project-based course. You will have created each of the major model types (Autoregressive, Moving Average, ARMA, ARIMA, and decomposition) using a real-world data set to anticipate the future by the end of this chúng tôi project focuses on time series data analysis in Python for beginners. Only after conducting thorough exploratory research and gaining insight into the data set is model construction effective. The following are the goals: 1. Importing needed libraries and time-series data sets. 2. Review the summary of time-series data and obtain basic descriptive statistics. 3. Make inferences from time-series data visualization graphs 4. Examine how time series data behaves. 5. Convert non-stationary data to stationary data using transformation chúng tôi the basis of historical data, predictive models seek to forecast future value. You will analyze the global transmission of the Covid-19 virus and train a time-series model (fbprophet) to predict corona virus-related infections in the United States in this hands-on chúng tôi specialization will go through basic predictive modeling approaches for estimating important parameter values, as well as optimization and simulation approaches for formulating judgments based on those parameter values and situational restrictions. The specialization will teach how to use predictive models, linear optimization, and simulation methods to model and solve decision-making problems.
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Random Forest For Time Series Forecasting
This article was published as a part of the Data Science Blogathon
Introductiontechnique. It is an ensemble learning method, constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the individual trees. It can be used for both Classification and Regression problems in ML. However, it can also be used in time series forecasting, both univariate and multivariate dataset by creating lag variables and seasonal component variables manually.
No algorithm works best for all the datasets. So depending on the data you can try various algorithms and choose the best for your data. I have tried ARIMA, SARIMA, ets, lstm, Random forest, XGBoost, and fbprophet for time series forecasting and each of these algorithms worked best for one category or the other. Random forest, XGBoost, and fbprophet outperformed for multivariate and intermittent data.
Intermittent data:Intermittent demand data is one of the data types with a very random pattern, for example, demand data. The data will have a value (not zero) if there is a demand. If there is no demand, the data is zero. Intermittent demand data is usually called customer demand data or sales data for an item that is not sold every time.
In this tutorial, you will learn how to develop a Random forest model for time series forecasting.
After completing this tutorial, you will know:
How to develop a Random Forest model for univariate/multivariate time series data.
How to limit the number of independent variables to a certain value.
How to forecast for multiple date points e.g. for the coming 4 months or 4 weeks.
Let’s get started.
Problem: Forecast demand for a jeans brand for the coming 6 months.
Data: We have monthly sales quantity available for 2 years (from May 2023 to May 2023) in the CSV file.
Import all required Packages import pandas as pd from sklearn.feature_selection import RFE from sklearn.ensemble import RandomForestRegressor from pandas import DataFrame import numpy as np from datetime import timedelta import calender jeans_data=pd.read_csv('jeans_data.csv') jeans_data.head()date SaleQty
2023-05-01 1683
2023-06-01 1321
2023-07-01 1447
2023-08-01 0
2023-09-01 86
2023-10-01 1165
Check if the data is stationary from statsmodels.tsa.stattools import adfuller from numpy import log result = adfuller(df.value.dropna()) print('p-value: %f' % result[1])p-value: 0.024419
Since the p-value is below 0.05, the data can be assumed to be stationary hence we can proceed with the data without any transformation.
Create lag variables dataframe = DataFrame() for i in range(12, 0, -1): dataframe['t-' + str(i)] = jeans_data.SaleQty.shift(i) final_data = pd.concat(jeans_data, dataframe], axis=1) final_data.dropna(inplace=True)You can give any value in place of 12, depending on your time interval and the number of lags you want to create. It is ideal to give 12 for monthly data and 54 for weekly data and limit the number of independent variables later.
Add seasonal variableCreate a variable that has different values for different months which will add a seasonal component to the model, which may help improve the forecast.
final_data['date'] = pd.to_datetime(final_data['date'], format='%Y-%m-%d') final_data['month'] = final_data['date'].dt.monthOr we can add dummy variables for each month:
dummy = pd.get_dummies(final_data['month']) final_data = pd.concat([final_data, dummy], axis=1) Train the model:We will take the most recent 6 months data as the test dataset and the rest of the data as the training dataset.
finaldf = final_data.drop(['date'], axis=1) finaldf = finaldf.reset_index(drop=True) test_length=6 end_point = len(finaldf) x = end_point - test_length finaldf_train = finaldf.loc[:x - 1, :] finaldf_test = finaldf.loc[x:, :] finaldf_test_x = finaldf_test.loc[:, finaldf_test.columns != 'SaleQty'] finaldf_test_y = finaldf_test['SaleQty'] finaldf_train_x = finaldf_train.loc[:, finaldf_train.columns != 'SaleQty'] finaldf_train_y = finaldf_train['SaleQty'] print("Starting model train..") rfe = RFE(RandomForestRegressor(n_estimators=100, random_state=1), 4) fit = rfe.fit(finaldf_train_x, finaldf_train_y) y_pred = fit.predict(finaldf_test_x)I have used RFE (recursive feature elimination) to limit the number of independent variables/features to 4, you can change the value and choose the value that gives the least error. I have taken n_estimators (number of trees in the forest) 100 which is the default value.
Evaluating the Algorithm: y_true = np.array(finaldf_test_ y['SaleQty']) sumvalue=np.sum(y_true) mape=np.sum(np.abs((y_true - y_pred)))/sumvalue*100 accuracy=100-mape print('Accuracy:', round(accuracy,2),'%.')Accuracy: 89.42 %.
Predict for Future:We will predict sale quantity for the future 6 months. The lags will be null for future date points so we have to predict for one month at a time and use the predicted sale for creating lag for next month’s prediction and so on. Please note we are using the predicted sale only to create the lag variable, we are not training the model again.
def create_lag(df3): dataframe = DataFrame() for i in range(12, 0, -1): dataframe['t-' + str(i)] = df3.SaleQty.shift(i) df4 = pd.concat([df3, dataframe], axis=1) df4.dropna(inplace=True) return df4 yhat=[] future_dataframe= jeans_data.copy() n=6 x = future_dataframe.at[end_point - 1, 'date'] days_in_month=calendar.monthrange(x.year, x.month)[1] for i in range(n): future_dataframe.at[future_dataframe.index[end_point + i], 'date'] = x + timedelta(days=days_in_month + days_in_month * i) future_dataframe.at[future_dataframe.index[end_point + i], SaleQty] = 0 future_dataframe ['date'] = pd.to_datetime(future_dataframe ['date'], format='%Y-%m-%d') future_dataframe ['month'] = future_dataframe ['date'].dt.month future_dataframe = future_dataframe.drop(['date'], axis=1) future_dataframe _end = len(jeans_data) for i in range(n, 0, -1): y = future_dataframe _end - i inputfile = finaldf.loc[y:end_point, :] inputfile_x = inputfile.loc[:, inputfile.columns != 'SaleQty'] pred_set = inputfile_x.head(1) pred = fit.predict(pred_set) future_dataframe.at[future_dataframe.index[future_dataframe _end - i], 'SaleQty'] = pred[0] finaldf = create_lag(future_dataframe) finaldf = finaldf.reset_index(drop=True) yhat.append(pred) predicted_value= np.array(yhat)You can add any other independent variables available like promotions, special_days, weekends, start_of_month, etc.
Find below the complete code:
import pandas as pd from sklearn.feature_selection import RFE from sklearn.ensemble import RandomForestRegressor from pandas import DataFrame import numpy as np from datetime import datetime import calendar from datetime import timedelta import datetime as dt def add_month(df, forecast_length, forecast_period): end_point = len(df) df1 = pd.DataFrame(index=range(forecast_length), columns=range(2)) df1.columns = ['SaleQty', 'date'] df = df.append(df1) df = df.reset_index(drop=True) x = df.at[end_point - 1, 'date'] x = pd.to_datetime(x, format='%Y-%m-%d') days_in_month=calendar.monthrange(x.year, x.month)[1] if forecast_period == 'Week': for i in range(forecast_length): df.at[df.index[end_point + i], 'date'] = x + timedelta(days=7 + 7 * i) df.at[df.index[end_point + i], 'SaleQty'] = 0 elif forecast_period == 'Month': for i in range(forecast_length): df.at[df.index[end_point + i], 'date'] = x + timedelta(days=days_in_month + days_in_month * i) df.at[df.index[end_point + i], 'SaleQty'] = 0 df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d') df['month'] = df['date'].dt.month df = df.drop(['date'], axis=1) return df def create_lag(df3): dataframe = DataFrame() for i in range(12, 0, -1): dataframe['t-' + str(i)] = df3.SaleQty.shift(i) df4 = pd.concat([df3, dataframe], axis=1) df4.dropna(inplace=True) return df4 def randomForest(df1, forecast_length, forecast_period): df3 = df1[['SaleQty', 'date']] df3 = add_month(df3, forecast_length, forecast_period) finaldf = create_lag(df3) finaldf = finaldf.reset_index(drop=True) n = forecast_length end_point = len(finaldf) x = end_point - n finaldf_train = finaldf.loc[:x - 1, :] finaldf_train_x = finaldf_train.loc[:, finaldf_train.columns != 'SaleQty'] finaldf_train_y = finaldf_train['SaleQty'] print("Starting model train..") rfe = RFE(RandomForestRegressor(n_estimators=100, random_state=1), 4) fit = rfe.fit(finaldf_train_x, finaldf_train_y) print("Model train completed..") print("Creating forecasted set..") yhat = [] end_point = len(finaldf) n = forecast_length df3_end = len(df3) for i in range(n, 0, -1): y = end_point - i inputfile = finaldf.loc[y:end_point, :] inputfile_x = inputfile.loc[:, inputfile.columns != 'SaleQty'] pred_set = inputfile_x.head(1) pred = fit.predict(pred_set) df3.at[df3.index[df3_end - i], 'SaleQty'] = pred[0] finaldf = create_lag(df3) finaldf = finaldf.reset_index(drop=True) yhat.append(pred) yhat = np.array(yhat) print("Forecast complete..") return yhat predicted_value=randomForest(jeans_data, 6, 'Month')Random forest is an ensemble learning method and it does bootstrap of observations where the training set is sampled randomly. So the order of the data points change hence it might not perform well in many time series data, but it does perform well for intermittent data as it catches the probability of demand/sale of a zero selling product well.
Please let me know your queries and suggestions if any.
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Top 10 Edge Ai Trends To Watch Out For In 2023
The top Edge AI trends in 2023 help increase efficiency, reduce cost, grow customer satisfaction
Many organisations see Artificial Intelligence as the solution to a lot of uncertainty like economic uncertainty, labour shortages, supply chain challenges, etc, bringing improved efficiency, differentiation, automation, and cost savings to airports, stores, and hospitals, among other places, which is why Edge AI trends have been accelerated.
Edge AI is AI that operates locally rather than in the cloud. Because of lightweight models and lower-cost high-performance GPUs, its implementation will become more accessible and less expensive in 2023. Edge AI enables the powering of scalable, mission-critical, and private AI applications. Because Edge AI is a new technology, many Edge AI applications are expected in the near future such as AI healthcare, Smart AI vision, Smart energy, and intelligent transportation system. According to Markets and Markets Research, the global Edge AI software market will grow from $590 million in 2023 to $1.83 trillion by 2026. Let’s take a look at the top 10 Edge AI Trends in 2023:
Focus on AI use cases with High ROI
Machine learning with Automation
Edge AI in Safety
AI functional safety is related to the trend of human-machine collaboration. More companies are looking to use AI to add proactive and flexible safety measures to industrial environments, as seen in autonomous vehicles. The functional safety has been used in industrial settings in a binary fashion, with the primary role of the safety function being to immediately stop the equipment from causing any harm or damage when an event is triggered.
AI in Cybersecurity
The increasing use of AI in security operations is the next logical step in the evolution of automated defences against cyber threats. The use of artificial intelligence (AI) in cybersecurity extends beyond the capabilities of its forerunner, automation, and includes tasks like the routine storage and safeguarding of sensitive data.
Edge AI picks up momentum
AI was once considered experimental, but according to IBM research, 35% of companies today report using AI in their business, with an additional 42% exploring AI. Edge AI use cases can help improve efficiency and lower costs, making them an appealing place to direct new investments. Supermarkets and big box stores, for example, are investing heavily in AI at self-checkout machines to reduce loss due to theft and human error.
Extensive use of AI in Process Discovery
Increased growth of AI on 5G
Edge AI along with new data processing and automation capabilities, supports a diverse ecosystem of evolving networks in ways that cloud-based solutions cannot. Furthermore, self-driving cars, virtual reality, and any other use case that requires real-time alerts require Edge AI and 5G for the fast processing it promises. As a result, 5G is promoting the Edge.
IoT growth driving Edge AI
Due to the limited data storage and computational power of these resource-constrained devices, performing deep learning in low-power IoT devices has always been difficult. Edge AI models are now cost-effective enough to operate at the edge, allowing devices to complete their own data processing and generate insights without relying on cloud-based AI.
Connecting Digital Twins to the Edge
The term “digital twin” refers to physically accurate virtual representations of real-world assets, processes, or environments that are perfectly synchronized. The explosion of IoT sensors and data that is driving both of these trends is what connects digital twins to the physical world and edge computing.
Creating Art with NFTs
Apple Watch Series 2 (Should You Buy In 2023)
Last Updated on July 22, 2023
Apple Watch Series 2 has been out for quite some time now, having been released in September 2023. It features a larger display, fitness tracking capabilities, and more tools than previous iterations. So is it worth buying or should you go for a newer model in 2023?
The Apple Watch Series 2 was released in 2023
Fitness TrackingOne of the main reasons why you may want an Apple Watch these days is its fitness tracking. The Series 2 has tracking abilities for heart rate, different types of exercise, and even sleep. It is also water-resistant so that you can use it while swimming.
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While those are some clear improvements made in the move from the Apple Watch Series 1 This watch does not support cellular data, so you will need your phone with you at all times if you want your watch to record GPS.
Your phone will also need to be connected to cellular data or WiFi. Newer models have cellular connectivity themselves, but the Series 2 does not. So if this is an important feature to you, the Series 2 might not be the best fit.
Current CostThe Apple Watch Series 2 is 6 years old now, so the price has reduced considerably. This makes it one of the more affordable Apple Watches, suitable for anyone on a budget, but with the obvious caveats and availability considerations.
But, essentially, if major retailers no longer stock a device, you may want to try and stretch our budget to something more modern.
SupportApple itself no longer sells the Series 2 on its website anymore, which is usually a good rule of thumb when considering the outlay of an older model.
Apple releases updates for a minimum of five years for all of their products so that only ensured that new updates were being released until 2023.
At the time of writing, the Series 2 can still be updated to watchOS 6.3, but will not update beyond that. It also won’t be compatible with devices using iOS 15 or later. For that, you’ll need to look at the Apple Watch Series 3.
So yes, this watch is great for people who want the basic function of an Apple Watch without spending too much money on one. But you still need the right iPhone ecosystem to get the most out of it.
Is The Apple Watch Series 2 Worth It In 2023?If you are looking for a smartwatch that offers a lot of functionality, then the Apple Watch Series 2 probably isn’t for you. However, if you just want something simple that tracks your activity, then you might find the Apple Watch Series 2 useful if you have an older iPhone too.
The key consideration? You will always need to be close to your phone if you want to make the most of your Series 2 watch, as you will need your watch to piggyback off of the data or WiFi capabilities of your phone.
With the Series 7 Apple Watch available, it’s really worth considering if being five iterations behind is a good move when spending your money.
Other Options For YouIf you want your watch to be independent from your phone, the Series 2 isn’t going to cut it. However, this doesn’t mean that you have to jump straight to the Apple Watch Series 7. Instead, you can opt for the Series 3.
The Series 3 Apple Watch has cellular capability, as do the newer watches, but it is also the cheapest. It has similar capabilities to the Apple Watch Series 2 too, so you’re really only paying a slightly higher price for the cellular ability.
Plus, there will be updates to WatchOS 8 for the Series 3, meaning there’s more life left in it yet.
Apple Watch Series 2 – SummaryOverall, the Apple Watch Series 2 is an okay option for anyone looking for a simple Apple Watch without the hefty price tag that the newer ones come with. It tracks your fitness, activity levels, heart rate, and more.
The only issues with the Apple Watch Series 2 are that you cannot use it independently without a connection with your phone, and the lack of updates. If you can get on board with these slight drawbacks, then it may be for you.
Top 10 Online Customer Analytics Courses To Master In 2023
Customer analytics courses take you through analytics to help you make informed business decisions
Customer analytics helps businesses break big problems into manageable answers. When companies need to look at how their customers behave, either as individuals or overall, customer analytics decodes their actions so that they’re easier to understand. This helps companies make better decisions on pricing, promotion, and management. Customer analytics is the process companies use to capture and analyze customer data to make better decisions. This article lists the top 10 customer analytics courses as available in Coursera.
Offered by: University of Pennsylvania In this course, four of Wharton’s top marketing professors will provide an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few. This course provides an overview of the field of analytics so that you can make informed business decisions. It is an introduction to the theory of customer analytics and is not intended to prepare learners to perform customer analytics.
Offered by: Northwestern Throughout the five courses, you will explore how great leaders assess themselves and lead collaborative teams that effectively manage negotiations and conflict. You will discover how leaders communicate through storytelling and employ other communication strategies to influence. Furthermore, You will learn how organizations start with the clarity of purpose that comes from an understanding of customers’ needs, including leveraging data analytics and using that focus to drive the design of products and services to meet those needs effectively.
Offered by: Macquarie University
Offered by: Coursera Project Network This project is an introductory level course intended for business professionals who would like to collect employee feedback using interactive forms. The project will focus on the basics of creating an interactive form. We will learn about the different types of questions available, themes. and sharing our survey. At the end of the project, the learner will be able to design their professional dynamic form for collecting employee feedback. This project will talk briefly about sharing the survey with respondents.
Offered by: University of Virginia This course, developed at the Darden School of Business at the University of Virginia, gives you the tools to measure brand and customer analytics assets, understand regression analysis, and design experiments as a way to evaluate and optimize marketing campaigns. You’ll leave the course with a solid understanding of how to use marketing analytics to predict outcomes and systematically allocate resources.
Offered by: University of Pennsylvania This Specialization provides an introduction to big data analytics for all business professionals, including those with no prior analytics experience. You’ll learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and you’ll develop basic data literacy and an analytic mindset that will help you make strategic decisions based on data.
Offered by: Institute for Gender and the Economy In this course, you will examine how policies, products, services & processes have gendered impacts that miss opportunities or create needless risks, break norms that perpetuate exclusion in serving customers/beneficiaries, get comfortable with concepts such as sex, gender identity & intersectionality, learn qualitative & quantitative analytical techniques to uncover intersectional gender-based insights, use human-centered design to create innovative solutions, become a transformational leader.
Offered by: ESSEC Business School This specialization is designed for students, business analysts, and Offered by: Coursera Project Network In this hands-on guided project, you will be trained on unsupervised Offered by: Rutgers the State University of New Jersey
Top Big Data/Data Science Job Openings In Adobe To Watch Out For This Month
Land a career in Adobe with these top big data/data science jobs.
Many businesses encountered turbulence in 2023, yet big data/data science saw substantial demand and growth.
Data science
professionals are in high demand all across the world. These job opportunities will continue to grow after 2023, with over 1.5 lakh more positions being added. This is a natural reaction to data’s importance as a resource for businesses in the digital age. We’ve compiled a list of the top 10
Big Data
/Data Science job openings in Adobe to watch out for this month.
Big Data DeveloperLocation:
Bangalore
Requirements:
5+ years in the design and development of large-scale data-driven systems.
Work experience with one or more big data technologies such as Apache Spark.
Work experience with one or more NoSQL storage systems such as Aerospike, HBase, Cassandra.
Contribution to open source is desirable.
Great problem solving, coding (in Java/Scala, etc.), and system design skills.
Know more
here
.
Data ScientistLocation:
Noida, Uttar Pradesh
Responsibilities:
Perform exploratory data analysis quickly, generate and test working hypotheses, and discover new trends and relationships.
Reports and presentations can be used to communicate results and educate others.
Know more
here
.
Senior Data EngineerLocation:
Bengaluru, Karnataka
Responsibilities:
Develop distributed data processing pipelines using Apache Spark. Build and maintain pipelines as needed to power critical business metrics to measure the performance of pages on the website.
Responsible for crafting, developing sophisticated data applications/pipelines on large-scale data platforms using Apache Spark, Hadoop, Python/Scala.
Know more
here
.
Computer Scientist – PythonLocation:
Bengaluru, Karnataka
Responsibilities:
Developing Java backend services that would make use of and add value to Adobe’s own data platform.
Building the company’s tracking services in a cookie-less world.
Know more
here
.
Web & Data Science AnalystLocation:
Noida, Uttar Pradesh
Responsibilities:
Selecting features, building and optimizing classifiers using machine learning techniques.
Data mining using state-of-the-art methods.
Doing ad-hoc analysis and communicating results in a clear manner.
Crafting automated anomaly detection systems and constant tracking of its performance.
Know more
here
.
Computer ScientistLocation:
San Francisco
Responsibilities:
Build high-performance and resilient micro-services for event and data processing at scale.
Design new features and create functional specifications by working with product management and engineering team members.
Develop software solutions by understanding the company’s customer’s requirements, data flows, and integration architectures.
Know more
here
.
Data Scientist/Senior Product Analyst, ExperimentationLocation:
San Jose
Responsibilities:
You will work with data engineers to design and automate data pipelines to scale experimentation and user analytics.
In collaboration with a multi-functional team of product management, marketing, and engineering, you will tap into the underlying data, align on metrics/methodologies and generate insights to develop valuable, highly effective programs.
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here
.
Web Analyst & Data ScienceLocation:
Bangalore
Responsibilities:
Responsible for providing Analytical Insights & Intelligence support aligned towards business or project or initiative.
Drive partnership with US Web Analytics team, Go-To-Market teams, eCommerce teams, chúng tôi Product Managers team, etc., and be the Subject Matter Expert for aligned areas.
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here
.
Adobe Analytics – Big Data Software DeveloperLocation:
Bucharest
Responsibilities:
Transform the business requirements into feature specifications.
Contribute to the design and implementation of new features.
Design and implement new features, APIs, unit and integration test suites.
Be involved in all the product development and delivery stages, as part of a unified engineering team.
Data EngineerLocation:
San Jose
Responsibilities:
Design, develop & tune data products, applications, and integrations on large-scale data platforms (Hadoop, Snowflake, Alteryx, SSIS, Kafka Streaming, Hana, SQL server) with an emphasis on performance, reliability, and scalability, and most of all quality.
Analyze the business needs, profile large data sets and build custom data models and applications to drive the Adobe business decision making and customers experience.
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