Top 10 In-Demand IT Skills for 2019: From DevOps to AI and Cloud

1. DevOps

DevOps engineers are essential for automating software delivery and infrastructure management. Core competencies include:

CI/CD pipelines using Jenkins, GitLab CI or GitHub Actions.

Configuration‑management and orchestration tools such as Puppet, Chef, Ansible and SaltStack.

Container technologies ( Docker) and orchestration platforms ( Kubernetes).

Monitoring and alerting with Nagios, Prometheus and Grafana.

Version control using Git.

Industry‑recognised certifications (e.g., AWS DevOps Engineer, Docker Certified Associate) demonstrate proficiency.

2. Big Data & Hadoop

Hadoop remains a foundational platform for large‑scale storage and batch processing. Practitioners should understand:

Hadoop Distributed File System (HDFS) architecture and data replication strategies.

MapReduce programming model and job scheduling.

Key ecosystem components: Hive (SQL‑like querying), Pig (data flow scripting), Sqoop (data import/export), Oozie (workflow scheduler), and YARN (resource management).

Emerging alternatives for real‑time processing such as Apache Spark and Flink.

Hands‑on experience deploying Hadoop clusters (on‑premise or cloud) is critical.

3. Python & Django

Python’s rapid growth makes it a primary language for web development, data analysis, and automation. Key areas:

Core language features: data structures, generators, decorators, and type hinting.

Web development with the Django framework: project scaffolding, URL routing, ORM usage, template rendering, and security best practices.

Testing with pytest or Django’s built‑in test suite.

Packaging and dependency management via pip, virtualenv or poetry.

Building a simple Django app (e.g., a blog) illustrates the MVC pattern and RESTful API creation.

4. Data Science with R & Python

R and Python are the dominant languages for statistical analysis and machine learning. Practitioners should be able to:

Perform data cleaning, transformation, and exploratory analysis using pandas (Python) or dplyr (R).

Visualise data with matplotlib, seaborn, ggplot2 or plotly.

Apply statistical tests, regression models, and time‑series forecasting.

Develop reproducible pipelines with Jupyter notebooks or R Markdown.

Deploy models on cloud platforms (AWS SageMaker, Azure ML) or containerised environments.

Working with real‑world datasets (e.g., from Amazon, Facebook, Adobe, Walmart) reinforces practical skills.

5. Machine Learning

Machine‑learning engineers need a solid grounding in algorithms, model engineering, and productionisation. Essential topics include:

Supervised learning (linear/logistic regression, decision trees, ensemble methods, SVMs).

Unsupervised learning (clustering, dimensionality reduction).

Deep learning frameworks: TensorFlow, Keras, PyTorch.

Feature engineering, data preprocessing, and model evaluation metrics (accuracy, ROC‑AUC, F1‑score).

Deployment patterns: REST APIs, batch scoring, and edge inference.

Certification paths such as the TensorFlow Developer Certificate help validate these skills.

6. Artificial Intelligence

AI expands beyond machine learning to include reasoning, planning, and perception. Professionals should be familiar with:

Fundamental AI concepts: search algorithms, knowledge representation, and probabilistic reasoning.

Parallel and distributed computing for large‑scale AI workloads (e.g., using GPUs, TPUs, or Spark).

Core sub‑domains: computer vision, natural language processing, and reinforcement learning.

Algorithmic optimisation and data‑mining techniques.

Strong foundations in computer science and mathematics (linear algebra, calculus, statistics) are prerequisites.

7. Robotic Process Automation (RPA)

RPA tools automate repetitive business processes. Key platforms to master: UiPath: workflow design, selectors, and Orchestrator management. Automation Anywhere: bot creation, meta‑bots, and analytics. Blue Prism: process studio, object studio, and secure credential handling.

Practical experience building end‑to‑end bots (e.g., invoice processing) demonstrates competency.

8. Cloud Computing – AWS

AWS dominates the public‑cloud market; professionals should understand core services and architectural patterns:

Compute: EC2, Lambda (serverless), Elastic Beanstalk.

Storage: S3, EBS, Glacier.

Databases: RDS, DynamoDB, Aurora.

Networking: VPC, Subnets, Security Groups, Load Balancers.

Infrastructure as Code with CloudFormation or Terraform.

Preparing for the AWS Certified Solutions Architect or AWS Certified DevOps Engineer exams helps close skill gaps.

9. Business Intelligence – Tableau

Tableau enables interactive data visualisation and dashboarding. Core capabilities to master:

Connecting to diverse data sources (SQL, CSV, cloud warehouses).

Creating calculated fields, parameters, and table calculations.

Designing filters, sets, and hierarchies for drill‑down analysis.

Combining data via blending and joins.

Publishing dashboards, managing permissions, and using Tableau Server/Tableau Online for collaboration.

Proficiency allows developers to translate raw data into actionable insights for business stakeholders.

10. Digital Marketing Analytics

Analytics skills enable marketers to measure campaign performance and optimise ROI. Important tools and concepts include:

Web analytics platforms such as Google Analytics and Adobe Analytics.

Key performance indicators (KPIs): conversion rate, bounce rate, customer acquisition cost, lifetime value.

UTM tagging, event tracking, and funnel analysis.

Social‑media listening and sentiment analysis using APIs (e.g., Twitter, Facebook Graph).

Data visualisation and reporting with Tableau or Power BI.

Hands‑on projects that integrate multiple data sources provide the most effective learning experience.