Everlytics Data Science Profile | Freelancer

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4,7

(16 commentaires)

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$50 USD / heure

Drapeau de INDIA

bangalore,

$50 USD / heure

Il est actuellement 8:25 PM ici

Rejoint le juillet 2, 2015

0 Recommandations

Everlytics Data Science

@winnow1

4,7

(16 commentaires)

7,2

7,2

92%

92%

$50 USD / heure

Drapeau de INDIA

bangalore,

$50 USD / heure

100 %

Travaux complétés

100 %

Suivant le budget

80 %

Dans les temps

16 %

Taux de réembauche

Specialist in Data Engineering, Big Data and ML

I have 18 yoe in ML, Big Data, BI and related data science works. Focused on helping a few niche technology companies in bringing their (mostly disruptive) ideas to life. A design thinker, responsive and collaborative individual with strong background in data science. My clients engage me to architect and develop solutions that have Big Data and/or Machine Learning as differentiating components. - Predictive Analytics & ML - Big Data Backends - Streaming Data Pipelines - Good Old ETL & BI Machine Learning: Regression, Association (apriori), Classification (decision trees, random forest, logit), Clustering (k-means) Python, Scikit-learn Dataiku, RapidMiner, Azure ML Studio, SageMaker Data Visualisation: Power BI, Tableau, Qlik, Klipfolio, Kibana DW and ETL: Snowflake, BigQuery, Athena, AirFlow, SSIS Stream Processing: Spark, Flume, Kafka, Kafka Streams, Kinesis Elasticsearch (ELK) I believe in simple and future-proof design. Putting trust and satisfaction before money.

Voir tous les Hadoop Consultants en India

Contactez Everlytics Data Science concernant votre travail

Connectez-vous pour discuter de tous les détails via la messagerie.

Portfolio

Tableau works

Tableau works

Tableau works

Tableau works

Tableau works

Tableau works

Tableau works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

1. Visualizing Big Data Dashboards on Real-time Data

More to come...

Custom Kibana Dashboards

1. Visualizing Big Data Dashboards on Real-time Data

More to come...

Custom Kibana Dashboards

This is for a Fintech client that wanted to build a Market Sentiment Visualisation and Trading Dashboard that provides a sentiment-based market outlook for all major financial products.

The engagement involved:
1. Design and build of a data pipeline and storage layer: Spark Streaming app that constantly reads the tweets and stores them in MongoDB
2. Algorithm to classify each tweet and assign a sentiment score (PySpark and Spark ML lib)
3. Visualization layer (in Tableau initially and moved to Highcharts later on)

About the SentiFin dashboard screenshots that you see:
1. Buy Sell signals based on Sentiment score
2. Top Tweets with -ve and +ve Sentiments

As usual, we enjoyed working on this!

Sentiment Mining in Finance

This is for a Fintech client that wanted to build a Market Sentiment Visualisation and Trading Dashboard that provides a sentiment-based market outlook for all major financial products.

The engagement involved:
1. Design and build of a data pipeline and storage layer: Spark Streaming app that constantly reads the tweets and stores them in MongoDB
2. Algorithm to classify each tweet and assign a sentiment score (PySpark and Spark ML lib)
3. Visualization layer (in Tableau initially and moved to Highcharts later on)

About the SentiFin dashboard screenshots that you see:
1. Buy Sell signals based on Sentiment score
2. Top Tweets with -ve and +ve Sentiments

As usual, we enjoyed working on this!

Sentiment Mining in Finance

Client:
One of the giants among the internet companies

What is done:
1. Implementation and monitoring of Elasticsearch-Kafka pipeline
- Here I made use of Kafka’s distributed nature to increase parallelism and improve on indexing rate for ES
- Kafka is used as a buffer to make Logs-to-ES pipeline run smoothly, help with sudden spikes on log generation side and downtime on ES side
- Handling multiple types of logs in the Kafka pipeline, for many different ES consumers.

2. Monitoring the cluster using Telegraph-InfluxDB-Grafana stack. Monitor load on each node (cpu/ram/disk) and alert using

Elasticsearch-Kafka-Grafana

Client:
One of the giants among the internet companies

What is done:
1. Implementation and monitoring of Elasticsearch-Kafka pipeline
- Here I made use of Kafka’s distributed nature to increase parallelism and improve on indexing rate for ES
- Kafka is used as a buffer to make Logs-to-ES pipeline run smoothly, help with sudden spikes on log generation side and downtime on ES side
- Handling multiple types of logs in the Kafka pipeline, for many different ES consumers.

2. Monitoring the cluster using Telegraph-InfluxDB-Grafana stack. Monitor load on each node (cpu/ram/disk) and alert using

Elasticsearch-Kafka-Grafana

Client:
One of the giants among the internet companies

What is done:
1. Implementation and monitoring of Elasticsearch-Kafka pipeline
- Here I made use of Kafka’s distributed nature to increase parallelism and improve on indexing rate for ES
- Kafka is used as a buffer to make Logs-to-ES pipeline run smoothly, help with sudden spikes on log generation side and downtime on ES side
- Handling multiple types of logs in the Kafka pipeline, for many different ES consumers.

2. Monitoring the cluster using Telegraph-InfluxDB-Grafana stack. Monitor load on each node (cpu/ram/disk) and alert using

Elasticsearch-Kafka-Grafana

Client:
One of the giants among the internet companies

What is done:
1. Implementation and monitoring of Elasticsearch-Kafka pipeline
- Here I made use of Kafka’s distributed nature to increase parallelism and improve on indexing rate for ES
- Kafka is used as a buffer to make Logs-to-ES pipeline run smoothly, help with sudden spikes on log generation side and downtime on ES side
- Handling multiple types of logs in the Kafka pipeline, for many different ES consumers.

2. Monitoring the cluster using Telegraph-InfluxDB-Grafana stack. Monitor load on each node (cpu/ram/disk) and alert using

Elasticsearch-Kafka-Grafana

2726453

ElasticSearch

ElasticSearch

ElasticSearch

ElasticSearch

ElasticSearch

ElasticSearch

ElasticSearch

Tableau works

Tableau works

Tableau works

Tableau works

Tableau works

Tableau works

Tableau works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

Power BI dashboard showcase:
1. Sales Dashboard
2. Site Traffic - Google Analytics
3. Ambulance and Emergency Services - SLA Dashboard
4. Project Portfolio Mgmt Dashboard
5. Project Spend Dashboard

Power BI works

1. Visualizing Big Data Dashboards on Real-time Data

More to come...

Custom Kibana Dashboards

1. Visualizing Big Data Dashboards on Real-time Data

More to come...

Custom Kibana Dashboards

This is for a Fintech client that wanted to build a Market Sentiment Visualisation and Trading Dashboard that provides a sentiment-based market outlook for all major financial products.

The engagement involved:
1. Design and build of a data pipeline and storage layer: Spark Streaming app that constantly reads the tweets and stores them in MongoDB
2. Algorithm to classify each tweet and assign a sentiment score (PySpark and Spark ML lib)
3. Visualization layer (in Tableau initially and moved to Highcharts later on)

About the SentiFin dashboard screenshots that you see:
1. Buy Sell signals based on Sentiment score
2. Top Tweets with -ve and +ve Sentiments

As usual, we enjoyed working on this!

Sentiment Mining in Finance

This is for a Fintech client that wanted to build a Market Sentiment Visualisation and Trading Dashboard that provides a sentiment-based market outlook for all major financial products.

The engagement involved:
1. Design and build of a data pipeline and storage layer: Spark Streaming app that constantly reads the tweets and stores them in MongoDB
2. Algorithm to classify each tweet and assign a sentiment score (PySpark and Spark ML lib)
3. Visualization layer (in Tableau initially and moved to Highcharts later on)

About the SentiFin dashboard screenshots that you see:
1. Buy Sell signals based on Sentiment score
2. Top Tweets with -ve and +ve Sentiments

As usual, we enjoyed working on this!

Sentiment Mining in Finance

Client:
One of the giants among the internet companies

What is done:
1. Implementation and monitoring of Elasticsearch-Kafka pipeline
- Here I made use of Kafka’s distributed nature to increase parallelism and improve on indexing rate for ES
- Kafka is used as a buffer to make Logs-to-ES pipeline run smoothly, help with sudden spikes on log generation side and downtime on ES side
- Handling multiple types of logs in the Kafka pipeline, for many different ES consumers.

2. Monitoring the cluster using Telegraph-InfluxDB-Grafana stack. Monitor load on each node (cpu/ram/disk) and alert using

Elasticsearch-Kafka-Grafana

Client:
One of the giants among the internet companies

What is done:
1. Implementation and monitoring of Elasticsearch-Kafka pipeline
- Here I made use of Kafka’s distributed nature to increase parallelism and improve on indexing rate for ES
- Kafka is used as a buffer to make Logs-to-ES pipeline run smoothly, help with sudden spikes on log generation side and downtime on ES side
- Handling multiple types of logs in the Kafka pipeline, for many different ES consumers.

2. Monitoring the cluster using Telegraph-InfluxDB-Grafana stack. Monitor load on each node (cpu/ram/disk) and alert using

Elasticsearch-Kafka-Grafana

Client:
One of the giants among the internet companies

What is done:
1. Implementation and monitoring of Elasticsearch-Kafka pipeline
- Here I made use of Kafka’s distributed nature to increase parallelism and improve on indexing rate for ES
- Kafka is used as a buffer to make Logs-to-ES pipeline run smoothly, help with sudden spikes on log generation side and downtime on ES side
- Handling multiple types of logs in the Kafka pipeline, for many different ES consumers.

2. Monitoring the cluster using Telegraph-InfluxDB-Grafana stack. Monitor load on each node (cpu/ram/disk) and alert using

Elasticsearch-Kafka-Grafana

Client:
One of the giants among the internet companies

What is done:
1. Implementation and monitoring of Elasticsearch-Kafka pipeline
- Here I made use of Kafka’s distributed nature to increase parallelism and improve on indexing rate for ES
- Kafka is used as a buffer to make Logs-to-ES pipeline run smoothly, help with sudden spikes on log generation side and downtime on ES side
- Handling multiple types of logs in the Kafka pipeline, for many different ES consumers.

2. Monitoring the cluster using Telegraph-InfluxDB-Grafana stack. Monitor load on each node (cpu/ram/disk) and alert using

Elasticsearch-Kafka-Grafana

2726453

ElasticSearch

ElasticSearch

ElasticSearch

ElasticSearch

ElasticSearch

ElasticSearch

ElasticSearch

Commentaires

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5,0

€35,00 EUR

Design data architecture of SaaS application collecting big data

Quality and professional work. Alot of knowledge of Big Data on Google Cloud Platform. Recommended!

Data Processing

Software Architecture

Niko T.

@Plexcellmedia

•

il y a 4 ans

3,4

$1 125,00 AUD

Python wrapper for R program via rpy2

Professional work and service.

D

Michael R.

@Dmo40EuG6ht9FYV6

•

il y a 7 ans

5,0

$200,00 USD

Project for winnow1

Amazing guys to work with .. with a great pool of skill set. It was really a good experience doing this project with Everlytics.

Machine Learning (ML)

+1 de plus

R

Russel R.

@russelrahim

•

il y a 7 ans

5,0

$6 950,00 AUD

Sports Modeller

Quality work, Hopefully we will have a long-term relationship moving forward.

Statistical Analysis

I

Sam M.

@imas123

•

il y a 7 ans

5,0

$1 815,00 USD

Project for winnow1

he was a professional, he has a great experience

Machine Learning (ML)

Amazon Web Services

+1 de plus

A

Amal S.

@amalsss

•

il y a 7 ans

Éducation

Fellowship Program [PhD]

Indian Institute of Management, Ahmedabad,

India

2005 - 2009

(4 ans)

B.S.

Indian Institute of Technology, Madras,

India

1998 - 2002

(4 ans)

Contactez Everlytics Data Science concernant votre travail

Connectez-vous pour discuter de tous les détails via la messagerie.

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Certifications

Preferred Freelancer Program SLA 1

100%

UK English 1

75%

Meilleures compétences

10

9

9

7

Machine Learning (ML)

6

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