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Companies to expedite data science projects raising more funds amidst COVID-19.The explosion of data, significantly generated by sensor-driven devices, is making data science a crucial
chúng tôiAmount Funded: US$100 million Transaction Type: Fund-II Lead Investor(s): Vulcan Capital, Adams Street Partners
Mode AnalyticsAmount Funded: US$33 million Transaction Type: Series D Lead Investor(s): H.I.G. Growth Partners
Data SutramAmount Funded: US$20 million Transaction Type: Seed Round Lead Investor(s): Indian Angel Network
PachydermAmount Funded: US$16 million Transaction Type: Series B Lead Investor(s): M12
NarrativeAmount Funded: US$8.5 million Transaction Type: Series A Lead Investor(s): G20 Ventures
Climax FoodsAmount Funded: US$7.5 million Transaction Type: Seed Round Lead Investor(s): At One Ventures, Manta Ray Ventures, S2G Ventures,
The explosion of data, significantly generated by sensor-driven devices, is making data science a crucial business analytics solution . The field of data science draws a variety of scientific tools, processes, algorithms and knowledge to extract data from structured and unstructured datasets that help identify meaningful patterns in it. Currently, the domain is increasingly utilized in almost all organizations, thus increasing the demand of data scientists capable of deriving actionable insight from a cluster of data. This will eventually lead to data-driven decisions and increase profitability, improve operational efficiency, business performance and workflows. Today, more and more organizations are looking to invest in data science thanks to its power of innovation, with data-driven tools and techniques. Here are the top data science funding that companies/startups have raised in August 2023.Amount Funded: US$100 million Transaction Type: Fund-II Lead Investor(s): Vulcan Capital, Adams Street Partners chúng tôi , an early-stage venture capital firm that invests in companies using models built through data science, closed US$100 million for its second fund. The fund was backed by Vulcan Capital, US private markets investment manager Adams Street Partners, and the family office of Marc Andreessen, and Chris Dixon. Founded by a team of data scientists and entrepreneurs, Fund II will enable chúng tôi to continue investing globally across different sectors and company stages, and amplify the company’s number of follow-on investments. According to the company, the first fund closed in 2023 was US$40 million in size.Amount Funded: US$33 million Transaction Type: Series D Lead Investor(s): H.I.G. Growth Partners Mode Analytics , which provides an online service for collaboratively analyzing data, raised US$33 million in Series D funding round led by HIG Growth Partners, with additional participation from Valor Equity Partners, Foundation Capital, REV Venture Partners and Switch Ventures. Reportedly, the company will use the funds to invest in its analytics platform, which combines analytics and business intelligence, data science and machine learning. Recently, Mode Analytics has started to introduce tools, including SQL and Python tutorials, for less technical users, especially those in product teams, so that they can structure queries that data scientists can subsequently execute faster and with more complete responses.Amount Funded: US$20 million Transaction Type: Seed Round Lead Investor(s): Indian Angel Network Data Sutram , an AI-based Location Intelligence Enterprise with a Cloud-based B2B product that works on a Data as a Service business model to help businesses, secured US$20 million in a Seed funding round from Indian Angel Network (IAN) angels, Uday Sodhi, Mitesh Shah and Nitin Jain. Founded in 2023 by three Jadavpur University engineering graduates and operated by Extrapolate Advisors Pvt Ltd, the startup helps companies by pinpointing new locations for them to expand, improve the performance of existing assets both physical and digital, and micro-target the right audience for their products.Amount Funded: US$16 million Transaction Type: Series B Lead Investor(s): M12 Pachyderm , an enterprise-grade, open-source data science platform, closed US$16 million in Series B funding round from Microsoft’s venture fund. This latest funding round comes as the company launches the general availability of Pachyderm Hub, a fully-managed service solution that has been operating in public beta since November, which is available today. According to Pachyderm, the fund will be used toward hiring which has become necessary as the coronavirus-spurred remote work shift has led to a major uptick in sales.Amount Funded: US$8.5 million Transaction Type: Series A Lead Investor(s): G20 Ventures Narrative , which empowers participants in the data economy, received US$8.5 million in Series A funding round to launch a new product designed to further simplify the process of buying and selling data. The round was led by G20 Ventures with existing backers Glasswing Ventures, MathCapital, Revel Partners, Tuhaye Venture Partners, and XSeed Capital. According to the company, this round of funding supports the launch of a new category: Data Streaming, which effectively replaces the broken data broker industry model with a transformative solution.Amount Funded: US$7.5 million Transaction Type: Seed Round Lead Investor(s): At One Ventures, Manta Ray Ventures, S2G Ventures, Climax Foods , a data science company, raised US$7.5 million in a Seed funding round to stimulate AI research into how plants can be converted into products. The round was led by At One Ventures, founded by GoogleX co-founder Tom Chi, along with Manta Ray Ventures, S2G Ventures, Valor Siren Ventures, Prelude Ventures, ARTIS Ventures, Index Ventures, Luminous Ventures, Canaccord Genuity Group, Carrot Capital and Global Founders Capital. Climax Foods aims to create a smart way to make food by converting plants, with less processing, into products with the same taste as animal-based products, at a price point accessible to everyone.
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Investment Alert: Top 5 Tech Stocks To Buy On August 16, 2023
Some popular, as well as new tech stocks, are thriving in the global tech market owing to the emergence of digital transformation and the tech-driven mindset of Industry 4.0. Cutting-edge technologies have started entering our lives to boost productivity in each and every way through multiple smart devices. There are upcoming tech companies that are creating new technologies to gain a competitive edge in this market. Investors are also aiming for these tech stocks to invest and earn revenue in a short period of time. Analytics Insight provides a list of the top 5 tech stocks, according to
Foxconn TechnologyCurrent price: US$63.50 Market cap: US$89.82 billion Foxconn Technology is a well-known Taiwanese multinational electronics contract manufacturer focused on producing and selling metal casings, parts, and components. It also offers hand-held device casings, metal parts, machine-related components, thermal modules, and many more. Foxconn is also focused on R&D, manufacture, as well as the marketing of optoelectronics and computer cables.
PanasonicCurrent price: US$12.40 Market cap: US$28.93 billion Panasonic Corporation is a world-known tech company focused on multiple electronic products across the world. There are five segments for the global target audience— appliances, life solutions, connected solutions, automotive, and industrial solutions. There are wide and diverse ranges of products available in the global tech market such as air conditioners, refrigerators, washing machines, digital cameras, showcases, water-related products, wiring devices, projectors, batteries, and many more.
LG ElectronicsCurrent price: US$150,500 Market cap: US$28.33 trillion LG Electronics is one of the popular tech companies to sell consumer electronics, mobile communications as well as home appliances across the world. There are five segments for the target audience— home appliance and air solution, home entertainment, mobile communications, vehicle components solutions, and business solutions. The company offers a diverse range of products to the tech-driven world such as air purifiers, air conditioners, washing machines, OLED and LED signages, commercial TVs, solar panels, compressors, and many more. It also provides a Cloud service to connect businesses and machines to business owners known as LG Smart Solutions.
LenovoCurrent price: US$20.46 Market cap: US$12.32 billion Lenovo is focused on developing, manufacturing, and marketing technology products and services across the global tech market through two segments— intelligent devices group and data center group. It operates in China, the Asia Pacific, Europe, the Middle East, Africa, and the Americas. Lenovo is popular for laptops, phones, software, computer hardware, management, supply chain, data management, investment management, and many more.
HitachiCurrent price: US$114.76 Market cap: US$55.49 billion
Some popular, as well as new tech stocks, are thriving in the global tech market owing to the emergence of digital transformation and the tech-driven mindset of Industry 4.0. Cutting-edge technologies have started entering our lives to boost productivity in each and every way through multiple smart devices. There are upcoming tech companies that are creating new technologies to gain a competitive edge in this market. Investors are also aiming for these tech stocks to invest and earn revenue in a short period of time. Analytics Insight provides a list of the top 5 tech stocks, according to Yahoo Finance Current price: US$63.50 Market cap: US$89.82 billion Foxconn Technology is a well-known Taiwanese multinational electronics contract manufacturer focused on producing and selling metal casings, parts, and components. It also offers hand-held device casings, metal parts, machine-related components, thermal modules, and many more. Foxconn is also focused on R&D, manufacture, as well as the marketing of optoelectronics and computer cables.Current price: US$12.40 Market cap: US$28.93 billion Panasonic Corporation is a world-known tech company focused on multiple electronic products across the world. There are five segments for the global target audience— appliances, life solutions, connected solutions, automotive, and industrial solutions. There are wide and diverse ranges of products available in the global tech market such as air conditioners, refrigerators, washing machines, digital cameras, showcases, water-related products, wiring devices, projectors, batteries, and many more.Current price: US$150,500 Market cap: US$28.33 trillion LG Electronics is one of the popular tech companies to sell consumer electronics, mobile communications as well as home appliances across the world. There are five segments for the target audience— home appliance and air solution, home entertainment, mobile communications, vehicle components solutions, and business solutions. The company offers a diverse range of products to the tech-driven world such as air purifiers, air conditioners, washing machines, OLED and LED signages, commercial TVs, solar panels, compressors, and many more. It also provides a Cloud service to connect businesses and machines to business owners known as LG Smart Solutions.Current price: US$20.46 Market cap: US$12.32 billion Lenovo is focused on developing, manufacturing, and marketing technology products and services across the global tech market through two segments— intelligent devices group and data center group. It operates in China, the Asia Pacific, Europe, the Middle East, Africa, and the Americas. Lenovo is popular for laptops, phones, software, computer hardware, management, supply chain, data management, investment management, and many more.Current price: US$114.76 Market cap: US$55.49 billion Hitachi is a popular Japanese tech company that provides IT, energy, industry, and smart life solutions beyond boundaries. It is focused on offering IoT, ATMs, self-service terminals, scanners, drone platform solutions, unmanned aerial system traffic management, and many more. It also offers medical equipment for radiation therapy, operates nuclear power plants, IGBT drives, PCs, UPS, steel systems, and many more to meet the needs of consumers.
Exploratory Data Analysis And Visualization Techniques In Data Science
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Exploratory Data Analysis (EDA) is a process of describing the data by means of statistical and visualization techniques in order to bring important aspects of that data into focus for further analysis. This involves inspecting the dataset from many angles, describing & summarizing it without making any assumptio
“Exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there” – John W. Tukey
Exploratory data analysis is a significant step to take before diving into statistical modeling or machine learning, to ensure the data is really what it is claimed to be and that there are no obvious errors. It should be part of data science projects in every organization.
This article was published as a part of the Data Science Blogathon
Why Exploratory Data Analysis is important?Just like everything in this world, data has its imperfections. Raw data is usually skewed, may have outliers, or too many missing values. A model built on such data results in sub-optimal performance. In hurry to get to the machine learning stage, some data professionals either entirely skip the exploratory data analysis process or do a very mediocre job. This is a mistake with many implications, that includes generating inaccurate models, generating accurate models but on the wrong data, not creating the right types of variables in data preparation, and using resources inefficiently.
In this article, we’ll be using Pandas, Seaborn, and Matplotlib libraries of Python to demonstrate various EDA techniques applied to Haberman’s Breast Cancer Survival Dataset.
Data set description Attribute information :
Patient’s age at the time of operation (numerical).
Year of operation (year — 1900, numerical).
A number of positive axillary nodes were detected (numerical).
Attributes 1, 2, and 3 form our features (independent variables), while attribute 4 is our class label (dependent variable).
Let’s begin our analysis . . .
1. Importing libraries and loading dataImport all necessary packages —
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import scipy.stats as statsLoad the dataset in pandas dataframe —
df = pd.read_csv('haberman.csv', header = 0) df.columns = ['patient_age', 'operation_year', 'positive_axillary_nodes', 'survival_status'] 2. Understanding data
Output:
Shape of the dataframe —
df.shapeThere are 305 rows and 4 columns. But how many data points for each class label are present in our dataset?
df[‘survival_status’].value_counts()Output:
The dataset is imbalanced as expected.
Out of a total of 305 patients, the number of patients who survived over 5 years post-operation is nearly 3 times the number of patients who died within 5 years.
df.info()Output:
All the columns are of integer type.
No missing values in the dataset.
2.1 Data preparationBefore we go for statistical analysis and visualization, we see that the original class labels — 1 (survived 5 years and above) and 2 (died within 5 years) are not in accordance with the case.
df['survival_status'] = df['survival_status'].map({1:"yes", 2:"no"}) 2.2 General statistical analysis df.describe()Output:
On average, patients got operated at age of 63.
An average number of positive axillary nodes detected = 4.
As indicated by the 50th percentile, the median of positive axillary nodes is 1.
As indicated by the 75th percentile, 75% of the patients have less than 4 nodes detected.
If you see, there is a significant difference between the mean and the median values. This is because there are some outliers in our data and the mean is influenced by the presence of outliers.
2.3 Class-wise statistical analysis survival_yes = df[df['survival_status'] == 'yes'] survival_yes.describe()Output:
survival_no = df[df[‘survival_status’] == ‘no’] survival_no.describe()
Output:
From the above class-wise analysis, it can be observed that —
The average age at which the patient is operated on is nearly the same in both cases.
Patients who died within 5 years on average had about 4 to 5 positive axillary nodes more than the patients who lived over 5 years post-operation.
Note that, all these observations are solely based on the data at hand.
3. Uni-variate data analysis 3.1 Distribution PlotsUni-variate analysis as the name suggests is an analysis carried out by considering one variable at a time. Let’s say our aim is to be able to correctly determine the survival status given the features — patient’s age, operation year, and positive axillary nodes count. Which among these 3 variables is more useful than other variables in order to distinguish between the class labels ‘yes’ and ‘no’? To answer this, we’ll plot the distribution plots (also called probability density function or PDF plots) with each feature as a variable on X-axis. The values on the Y-axis in each case represent the normalized density.
1. Patient’s age
sns.FacetGrid(df, hue = "survival_status").map(sns.distplot, "patient_age").add_legend() plt.show()Output:
Among all the age groups, the patients belonging to 40-60 years of age are highest.
There is a high overlap between the class labels. This implies that the survival status of the patient post-operation cannot be discerned from the patient’s age.
2. Operation year
sns.FacetGrid(df, hue = "survival_status").map(sns.distplot, "operation_year").add_legend() plt.show()Output:
Just like the above plot, here too, there is a huge overlap between the class labels suggesting that one cannot make any distinctive conclusion regarding the survival status based solely on the operation year.
3. Number of positive axillary nodes
sns.FacetGrid(df, hue = "survival_status").map(sns.distplot, "positive_axillary_nodes").add_legend() plt.show()Output:
This plot looks interesting! Although there is a good amount of overlap, here we can make some distinctive observations –
Patients having 4 or fewer axillary nodes — A very good majority of these patients have survived 5 years or longer.
Patients having more than 4 axillary nodes — the likelihood of survival is found to be less as compared to the patients having 4 or fewer axillary nodes.
But our observations must be backed by some quantitative measure. That’s where the Cumulative Distribution function(CDF) plots come into the picture.
The area under the plot of PDF over an interval represents the probability of occurrence of the random variable in the given interval. Mathematically, CDF is an integral of PDF over the range of values that a continuous random variable takes. CDF of a random variable at any point ‘x’ gives the probability that a random variable will take a value less than or equal to ‘x’.
counts, bin_edges = np.histogram(survival_yes[‘positive_axillary_nodes’], density = True) pdf = counts/sum(counts) cdf = np.cumsum(pdf) plt.plot(bin_edges[1:], cdf, label = ‘CDF Survival status = Yes’)
counts, bin_edges = np.histogram(survival_no[‘positive_axillary_nodes’], density = True) pdf = counts/sum(counts) cdf = np.cumsum(pdf) plt.plot(bin_edges[1:], cdf, label = ‘CDF Survival status = No’) plt.legend() plt.xlabel(“positive_axillary_nodes”) plt.grid() plt.show()
Output:
Some of the observations that could be made from the CDF plot —
Patients having 4 or fewer positive axillary nodes have about 85% chance of survival for 5 years or longer post-operation, whereas this number is less for the patients having more than 4 positive axillary nodes. This gap diminishes as the number of axillary nodes increases.
3.2 Box plots and Violin plotsBox plot, also known as box and whisker plot, displays a summary of data in five numbers — minimum, lower quartile(25th percentile), median(50th percentile), upper quartile(75th percentile), and maximum data values.
A violin plot displays the same information as the box and whisker plot; additionally, it also shows the density-smoothed plot of the underlying distribution.
Let’s make the box plots for our feature variables –
plt.figure(figsize = (15, 4)) plt.subplot(1,3,1) sns.boxplot(x = 'survival_status', y = 'patient_age', data = df) plt.subplot(1,3,2) sns.boxplot(x = 'survival_status', y = 'operation_year', data = df) plt.subplot(1,3,3) sns.boxplot(x = 'survival_status', y = 'positive_axillary_nodes', data = df) plt.show()Output:
The patient age and the operation year plots show similar statistics.
The isolated points seen in the box plot of positive axillary nodes are the outliers in the data. Such a high number of outliers is kind of expected in medical datasets.
Violin Plots –
plt.figure(figsize = (15, 4)) plt.subplot(1,3,1) sns.violinplot(x = 'survival_status', y = 'patient_age', data = df) plt.subplot(1,3,2) sns.violinplot(x = 'survival_status', y = 'operation_year', data = df) plt.subplot(1,3,3) sns.violinplot(x = 'survival_status', y = 'positive_axillary_nodes', data = df) plt.show()Output:
Violin plots in general are more informative as compared to the box plots as violin plots also represent the underlying distribution of the data in addition to the statistical summary. In the violin plot of positive axillary nodes, it is observed that the distribution is highly skewed for class label = ‘yes’, while it is moderately skewed for ‘no’. This indicates that –
For the majority of patients (in both the classes), the number of positive axillary nodes detected is on the lesser side. Of which, patients having 4 or fewer positive axillary nodes are more likely to survive 5 years post-operation.
These observations are consistent with our observations from previous sections.
4. Bi-variate data analysis 4.1 Pair plotNext, we shall plot a pair plot to visualize the relationship between the features in a pairwise manner. A pair plot enables us to visualize both distributions of single variables as well as the relationship between pairs of variables.
sns.set_style('whitegrid') sns.pairplot(df, hue = 'survival_status') plt.show()Output:
In the case of the pair plot, it can be seen that the plots on the upper half and lower half of the diagonal are the same, only the axis is interchanged. So, they essentially convey the same information. Analyzing either would suffice. The plots on the diagonal are different from the rest of the plots. These plots are kernel density smoothed histograms representing the univariate distribution of a particular feature.
As we can observe in the above pair plot, there is a high overlap between any two features and hence no clear distinction can be made between the class labels based on the feature pairs.
4.2 Joint plotWhile the Pair plot provides a visual insight into all possible correlations, the Joint plot provides bivariate plots with univariate marginal distributions.
sns.jointplot(x = 'patient_age', y = 'positive_axillary_nodes', data = df) plt.show()Output:
The pair plot and the joint plot reveal that there is no correlation between the patient’s age and the number of positive axillary nodes detected.
The histogram on the top edge indicates that patients are more likely to get operated in the age of 40–60 years compared to other age groups.
The histogram on the right edge indicates that the majority of patients had fewer than 4 positive axillary nodes.
4.3 HeatmapHeatmaps are used to observe the correlations among the feature variables. This is particularly important when we are trying to obtain the feature importance in regression analysis. Although correlated features do not impact the performance of the statistical model, it could mess up the post-modeling analysis.
Let’s see if there exist any correlation among our features by plotting a heatmap.
sns.heatmap(df.corr(), cmap = ‘YlGnBu’, annot = True)
plt.show()
Output:
The values in the cells are Pearson’s R values which indicate the correlation among the feature variables. As we can see, these values are nearly 0 for any pair, so no correlation exists among any pair of variables.
5. Multivariate analysis with Contour plotA contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, in a 2-dimensional format. A contour plot enables us to visualize data in a two-dimensional plot. Here is a diagrammatic representation of how the information from the 3rd dimension can be consolidated into a flat 2-D chart –
Plotting a contour plot using the seaborn library for patient’s age on x-axis and operation year on the y-axis —
sns.jointplot(x = 'patient_age', y = 'operation_year' , data = df, kind = 'kde', fill = True) plt.show()Output:
From the above contour plot, it can be observed that the years 1959–1964 witnessed more patients in the age group of 45–55 years.
Frequently Asked QuestionsIn this article, we learned some common steps involved in exploratory data analysis. We also saw several types of charts & plots and what information is conveyed by each of these. This is just not it, I encourage you to play with the data and come up with different kinds of visualizations and observe what insights you can extract from it.
About meHi, I am Pratik Nabriya, a Data Scientist currently employed with an Analytics & AI firm based out of Noida. My key skills include Machine learning, Deep learning, NLP, Time-Series Analysis, SQL and I’m familiar with working in a Cloud environment. I love to write blogs and articles in my spare time and share my learnings with fellow data professionals.
The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.
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Top 5 Chinese Smartphones For Under $100 – August 2023
Top 5 Chinese Smartphones for Under $100 1. Redmi 7
The most interesting specs are inside though, we indeed have a rather good Snapdragon 632 octa-core CPU, paired with a minimum of 2GB of 16GB of internal storage expandable via microSD.
In the camera department we find a primary 12MP shooter paired with a secondary 2MP sensor to snap photos with bokeh effect. Meanwhile at the front we have an 8MP selfie snapper.
Finally, the Redmi 7 is powered by a 4000mAh battery and runs MIUI 10 based on Android 9.
2. Redmi 7AFollowing the Redmi 7, we have its little brother, the more affordable Redmi 7A. The smaller phone comes with a 5,45-inch display of HD+ resolution and is powered by a Snapdragon 439 CPU coupled by up to 4GB of RAM and 64GB of storage (expandable).
Finally, the Redmi 7A packs a large 4000mAh capacity battery and runs MIUI 10 based on Android 9 Pie.
3. Lenovo K9 NoteIf you aren’t a fan of MIUI, then the Lenovo K9 Note is probably your best option. The handset packs a not too large 6-inch display with HD+ and a 18:9 aspect ratio. It’s powered by a good Qualcomm Snapdragon 450 CPU along with 3GB of RAM and 32GB of internal storage (expandable).
In the camera department we find two rear shooters of 16MP and 2MP resolution, with the secondary used to capture depth data and add the bokeh effect, meanwhile at the front we find an 8MP selfie snapper.
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Finally, the Lenovo K9 Note comes with a good sized 3760mAh capacity battery and runs Android 8.0 Oreo OTB.
4. Ulefone Note 7PAt around $80 the latest Ulefone Note 7P is a very interesting option in its price range. The smartphone the features a 6.1-inch of HD+ display with a 19.5:9 aspect ratio and is powered by a MediaTek Helio A22 along with 3GB of RAM and 32GB of internal storage (expandable).
Ulefone’s phone comes with three rear cameras with resolutions of 8MP, 2MP and 2MP. While in the notch at the front we find an 5MP selfie snapper.
Finally, the Ulefone Note 7P is fueled by a 3500mAh battery and runs Android 9.0 Pie out of the box.
5. UMIDIGI A5 ProFinally we conclude our list with the latest UMIDIGI A5 Pro, a smartphone that packs some hardware that would have been considered mid-range worthy not too long include. This includes a large 6.3-inch panel of Full HD+ resolution (yes, 1080 x 2280 px) and a good Mediatek Helio P23 paired with 4GB of RAM and 32GB of storage to power it up.
UMIDIGI did not skimp in the camera department either with a primary 16MP Sony IMX398 sensor, a secondary 8MP Samsung S5K4H7 (ultra wide) and a third 5MP sensor. While at the front we have a 16MP Samsung S5K3P3.
The UMIDIGI A5 Pro packs a large 4150mAh battery and runs Android 9.0 Pie OTB.
Best Chinese Smartphones for Under $100 Check out our top 5s below $200 and $500 over here!
Top Data Science Jobs In Faang Companies To Apply For In May
FAANG companies pay a fortune for data science jobs like data scientists and analysts
Over the past two decades,
Top Data Science Jobs in FAANG Companies Data Analyst, Product Support Operations at MetaLocation(s): Sunnyvale-CA, Austin-TX, Seattle-WA, Remote Roles and Responsibilities: Meta is seeking an experienced data analyst to join the team, who will focus on leading analytics efforts with cross-functional teams to improve the product experience by providing scalable data solutions, business intelligence, and analytics to PSO. The candidate should develop and manage end-to-end analytics solutions from requirements gathering, establishing key metrics, building a data pipeline, and dashboard, and analyzing data to drive the performance of operations. Qualifications:
The candidate should have 3+ years of experience in quantitative analysis, SQL, data visualization tools, etc.
He/she should experience communicating with a variety of audiences and stakeholders.
Apply
Data Analyst, Global Talent Selection at MetaLocation(s): Austin-TX, Washington-DC, New York, San Francisco-CA, Remote Roles and Responsibilities: The company is looking for data analysts who share their passion for building new functionality and improving existing systems. He/she should build new analytics and reporting capabilities to support program evaluation and operations. They should handle and how reporting analytics requests while addressing stakeholders’ long-term needs and driving insights for human resource partners and executives using existing dashboards. Qualifications:
The candidate should have 4+ years of experience working with SQL or relational databases.
Experience initiating and driving projects to completion with minimal guidance is mandatory.
He/she should have experience having effective conversations with clients about their support needs and requirements, managing the intake process, and asking the right questions to the scope and solve the requests.
Apply
Data Scientist at AppleLocation(s): Hyderabad, Telangana Roles and Responsibili]tes: As a data scientist at Apple, the candidate should design data science solutions to solve business challenges. He/she should be part of a large team, working on research and project delivery. They will apply statistical thinking and machine learning methods. The candidate should communicate complex concepts in intelligible ways. He/she will manage time and priorities in an exciting and changing environment. Qualifications:
The candidate should have 8-years of experience in data science and machine learning.
Hands-on programming language experience and proven competency in Python or R coding are mandatory.
He/she should have a good degree of knowledge about unstructured data analysis methodologies like NLP, NLG, etc.
Apply
Data Scientist- Strategic Data Solutions at AppleLocation(s): Austin, Texas Roles and Responsibilities: As an SDS data scientist, the candidate will employ predictive modeling and statistical analysis techniques to build end-to-end solutions for improving security, fraud prevention, and operational efficiency across the company, from manufacturing to fulfillment to apps and services. Qualifications:
The candidate should have practical experience with and theoretical understanding of algorithms for classification, regression, clustering, and anomaly detection.
He/she should have a working knowledge of relational databases including SQL, and large-scale distributed systems such as Hadoop and Spark.
They should have the ability to implement data science pipelines and applications in a general programming language such as Python, Scala, or Java.
Apply
Sr. Data Engineer, Data Solutions & Engineering, Security at AmazonLocation(s): London Qualifications:
The candidate should have a Bachelor’s degree in computer science, engineering, mathematics, or a related technical discipline.
He/she should have 8+ years of experience in data engineering, BI engineering, or a related field in architecting and developing end-to-end scalable data applications and data pipelines.
They should have 3+ years of coding experience with modern programming or scripting language.
Apply
Data Engineering Manager, Studio & Creative Production DSE at NetflixLocation(s): Los Gatos, California Roles and Responsibilities: As a data engineering manager, the candidate should achieve a consistently enjoyable experience for the company’s members through this process of a tall order. He/she should help visual effects teams deeply leverage data to inform their decision-making as they go about their tasks. They should help teams leverage data-driven methods such as reinforcement learning for artwork and video assets personalization and optimization methods for studio production workflows to drive better outcomes for the business. Qualifications:
The candidate should have been leading data engineering teams for 5+ years.
He/she should have a proven track record leading innovative, influential data engineering work in complex business domains.
They should be deeply invested in creating an inclusive team environment and helping each team members grow.
Apply
Data Scientist, Engineering at GoogleLocation(s): Mountain View-CA, Sunnyvale-CA, New York Qualifications:
The candidate should own a Master’s degree in a quantitative discipline.
He/she should have 2-years of experience in data analysis or a related field.
Experience with statistical software is mandatory.
Top 12 Data Observability Tools In 2023
Data accumulation is accelerating, with ~330 million terabytes of data created every day. To put this into perspective, a single terabyte can contain approximately 250,000 hours of music.
In this article, we have examined the top 12 data observability tools, based on their capabilities and features to help businesses in their vendor selection to find the best platform that suits their needs.
Data observability vs. data monitoringSource: Hayden James
Figure 1. Data monitoring vs. data observability
Before delving into the data observability tools capabilities, it’s critical to distinguish between data observability and data monitoring. While both aims to ensure data reliability and quality, their scope and approach differ.
Data monitoring is largely concerned with measuring certain metrics such as data pipeline performance, resource use, and processing times. It frequently takes a reactive strategy, with data teams responding to challenges as they arise.
Data observability, on the other hand, is a more comprehensive and proactive approach to analyzing and controlling data quality. It includes data monitoring but goes above and beyond by offering in-depth insights into the data itself, its lineage, and transformations. Data observability solutions allow data owners to identify and rectify issues before they have an influence on downstream processes and consumers, promoting data quality.
Data observability tools help data engineers to monitor, manage, and analyze their data pipelines, ensuring that data is accurate, timely, and consistent. Some key capabilities of data observability tools include:
1- Data lineage trackingThese tools can trace the origin and transformations of data as it moves through various stages in the data pipeline. This helps data analysts:
Identify dependencies
Understand the impact of changes,
Troubleshoot data quality issues
Save debugging time.
2- Automated monitoringData observability tools can continuously monitor and assess the quality of data based on predefined rules and metrics. This can include anomaly detection, data drift, and identifying data inconsistencies.
3- Real-time & customized alertsData observability tools can be integrated with communication platforms (e.g., Slack) and can send instant alerts and notifications to inform data scientists of potential issues.
4- Central data catalogingThese tools can automatically create and maintain a data catalog that documents all available data sources, their schemas, and metadata. This provides a central location for data teams to search and discover relevant data assets.
5- Data profilingData observability tools can analyze and summarize datasets, providing insights into the distribution of values, unique values, missing values, and other key statistics. This helps data teams understand the characteristics of their data and identify potential issues.
6- Data validationThese tools can run tests and validations against the data to ensure that it adheres to predefined business rules and data quality standards. This helps increase data health by catching errors and inconsistencies early in the data pipeline.
7- Data versioningData observability tools can track changes to data over time, allowing data teams to compare different versions of datasets and understand the impact of changes.
8- Data pipeline monitoringThese tools can monitor the performance and health of data pipelines, providing insights into processing times, resource usage, and potential bottlenecks. This helps data engineers to find and fix bad data to optimize their data pipelines for efficiency and scalability.
9- Collaboration and documentation 10- Integration with external data sourcesData observability tools can typically integrate with a wide range of data sources, processing platforms, and data storage systems, allowing data scientists to monitor and manage their data pipelines from a single unified interface.
11- Analytics & reportingData observability technologies can provide a variety of reports and visualizations to assist data teams in understanding the health of their data pipelines and the quality of their data. These findings can help guide decisions and enhance overall data management practices.
12- Instant customer supportMany data observability tools provide extensive customer service via different methods such as chat, email, and phone. Dedicated solutions engineers make sure that data teams have access to expert assistance anytime they encounter difficulties or require instruction on how to use the tool efficiently.
Vendor selection criteriaAfter identifying whether the vendors provide the capabilities presented above, we narrowed our vendor list based on some criteria. We used the number of B2B reviews and employees of a company to estimate its market presence because these criteria are public and verifiable.
Therefore, we set certain limits to focus our work on top companies in terms of market presence, selecting firms with
15+ employees
20+ reviews on review platforms including G2, Trustradius, Capterra
The following companies fit these criteria:
Databand
Metaplane
Monte Carlo
Mozart Data
Integrate.io
Anomalo
Datafold
Telmai
decube
Unravel Data
AccelData
Bigeye
As all vendors offer data cataloging, profiling, validation, versioning, and reporting, we did not include these capabilities in the table. Below you can see our analysis of data capability tools in terms of the capabilities and features mentioned above. You can sort Table 1, for example, by real-time alerting capabilities.
VendorsReviewsEmployee sizeStarting price/yearWarehouse integrationLineage trackingMonitored pipelinesReal-time alertingCustomer supportQuality of support* (out of 10) DataBand3539Not provided20+ data sourcesColumn-level100-1,000sEmail, Slack, Pagerduty, Opsgenie24 hour issue response and mitigation with a dedicated support channel9.2 Metaplane3715Pro: $9,900/year with monthly commitment options20+ data sourcesColumn-level lineage to BIUnlimitedEmail, Slack, PagerDuty, MS Teams, API, WebhooksShared Slack channel, CSM9.9 Monte Carlo71257Not provided30+ data sourcesField-levelNot providedN/ANot provided9.6 Mozart Data6932Starts from $12,000/year with monthly commitment options300+ data sourcesField-levelNot providedN/ANot provided9.5 Integrate.io18537Starts from $15,000/year150+ data sourcesField-levelNot providedN/AEmail, Chat, Phone, Zoom support9.2 Anomalo3349Not provided20+ data sourcesAutomated warehouse-to-BIUnlimited with unsupervised learningEmail, Slack, Microsoft TeamsNot provided9 Datafold2436Not provided12+ data sourcesColumn-levelNot providedEmail, SlackEmail, Intercom, dedicated Slack channel9.1 Telmai1513Not provided18+ data sourcesField-levelUnlimitedEmail, Slack, PagerDutyEmail9.2 decube1215Starts from $499 / year13+ data sourcesAutomatedNot providedEmail, SlackEmail, Chat8.3 Unravel Data23171Starts from $1 / per feature50+ data sourcesCode-levelNot providedEmailEmail8.6 AccelData12214Not provided30+ data sourcesColumn-levelNot providedAutomatedEmail8.6 Bigeye1569Not provided20+ data sourcesColumn-levelNot providedEmail, Slack, PagerDuty, MS Teams, WebhooksEmail7.9
*Based on G2 reviews.
Disclaimer:
The data is gathered from the websites of vendors. If you believe we have missed any material, please contact us so that we can consider adding it to our article.
Contact us if you need help in data observability tool selection:
Begüm Yılmaz
Begüm is an Industry Analyst at AIMultiple. She holds a bachelor’s degree from Bogazici University and specializes in sentiment analysis, survey research, and content writing services.
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