POSITION

:

DATA ANALYST

TASK & TARGETS

Tasks typically involve collecting, analysing, and interpreting data to help businesses make informed decisions. Here are some common tasks and targets of a data analyst:

1. Data Collection: Gathering data from various sources such as databases, spreadsheets, surveys, or online platforms. This may involve designing data collection methods and ensuring data quality and integrity.

2. Data Cleaning and Preprocessing: Identifying and addressing any errors, inconsistencies, or missing values in the data. This step may also involve transforming data into a suitable format for analysis.

3. Exploratory Data Analysis (EDA): Conducting initial data exploration to understand the patterns, relationships, and distributions within the data. This may involve using statistical techniques, data visualization tools, and summary statistics.

4. Data Modelling and Analysis: Applying statistical techniques and mathematical models to analyse the data. This could include regression analysis, clustering, classification, time series analysis, or predictive modelling.

5. Data Visualization: Creating visual representations of data to effectively communicate insights and findings. This includes using charts, graphs, and dashboards to present information in a clear and concise manner.

6. Reporting and Presentation: Summarizing and documenting analysis results in reports or presentations for stakeholders. Presenting findings in a way that is understandable to non-technical audiences.

7. Data-driven Decision Making: Collaborating with decision-makers and stakeholders to understand their requirements and provide actionable insights based on data analysis. Helping organizations make informed decisions and optimize processes.

8. Data Monitoring and Maintenance: Continuously monitoring data quality, identifying anomalies, and updating analysis as new data becomes available. Ensuring the accuracy and reliability of ongoing data processes.

The target of a data analyst is to provide meaningful insights and actionable recommendations based on data analysis to support business goals, improve decision-making processes, identify opportunities for improvement, optimize operations, and enhance overall performance.

It's important for data analysts to have a solid understanding of statistical methods, programming languages (such as Python or R), data visualization tools (like Tableau or Power BI), and database querying languages (such as SQL). Strong analytical and critical thinking skills, attention to detail, and effective communication abilities are also crucial for success in this role.

RESPONSIBILITIES

• Data Collection: Identify and gather relevant data from various sources, including databases, spreadsheets, APIs, and external platforms.
• Data Cleaning and Preprocessing: Clean, validate, and preprocess data to ensure accuracy, consistency, and completeness. Handle missing data, outliers, and inconsistencies appropriately.
• Exploratory Data Analysis (EDA): Conduct exploratory analysis to understand the patterns, trends, and relationships within the data. Use statistical techniques and visualization tools to identify key insights.
• Data Modelling and Analysis: Apply statistical methods, predictive models, and machine learning algorithms to analyse data and generate meaningful insights. Perform regression analysis, clustering, classification, time series analysis, or other relevant techniques as needed.
• Data Visualization: Create clear and visually appealing charts, graphs, and dashboards to present analysis results effectively. Communicate complex concepts and findings in a concise and understandable manner to non-technical stakeholders.
• Reporting and Presentation: Prepare comprehensive reports and presentations summarizing analysis findings, trends, and recommendations. Present insights to stakeholders, including management, clients, or other teams within the organization.
• Data-driven Decision Making: Collaborate with cross-functional teams and business stakeholders to understand their requirements and provide data-driven insights and recommendations. Help drive data-informed decision making to optimize business processes, strategies, and outcomes.
• Data Quality and Maintenance: Monitor data quality and integrity, proactively identify data issues, and take necessary steps to address them. Continuously update and maintain data analysis processes to incorporate new data sources and improve efficiency.
• Tool and Technology Expertise: Stay updated with the latest data analysis tools, techniques, and trends. Proficiently use programming languages (e.g., Python, R), statistical software, database querying languages (e.g., SQL), and data visualization tools (e.g., Tableau, Power BI).
• Collaboration and Communication: Work collaboratively with team members, data scientists, and stakeholders to understand business objectives and requirements. Clearly communicate analysis results, methodologies, and limitations to both technical and non-technical audiences.

QUALIFICATIONS

• Bachelor's degree in a quantitative field such as Statistics, Mathematics, Economics, Computer Science, or a related field. Master's or Ph.D. is a plus.
• Strong analytical and problem-solving skills with a keen attention to detail.
• Proficiency in programming languages commonly used in data analysis, such as Python or R.
• Experience with statistical analysis, data modelling, and visualization techniques.
• Familiarity with database querying languages (e.g., SQL) and data manipulation tools.
• Knowledge of machine learning concepts and algorithms is a plus.
• Excellent communication and presentation skills, with the ability to convey complex concepts in a clear and concise manner.
• Strong business acumen and the ability to translate data insights into actionable recommendations.
• Experience with data visualization tools such as Tableau or Power BI is desirable.
• Familiarity with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure) is a plus.